diff --git a/qbraid_sdk/aws_ionq_jobs_quantum_teleportation.ipynb b/qbraid_sdk/aws_ionq_jobs_quantum_teleportation.ipynb deleted file mode 100644 index 1d2f1b2..0000000 --- a/qbraid_sdk/aws_ionq_jobs_quantum_teleportation.ipynb +++ /dev/null @@ -1,645 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "8d4f7032-63b7-45e4-b7f6-3d6e3d467d90", - "metadata": {}, - "outputs": [], - "source": [ - "# This notebook is derived from Qiskit and includes modifications by qBraid.\n", - "#\n", - "# (C) Copyright IBM 2020.\n", - "# (C) Copyright qBraid 2023.\n", - "#\n", - "# This code is licensed under the Apache License, Version 2.0. You may\n", - "# obtain a copy of this license in the LICENSE.txt file in the root directory\n", - "# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n", - "#\n", - "# Any modifications or derivative works of this code must retain this\n", - "# copyright notice, and modified files need to carry a notice indicating\n", - "# that they have been altered from the originals." - ] - }, - { - "cell_type": "markdown", - "id": "7b310509-15b1-4679-8a3d-6ca9f4f17492", - "metadata": {}, - "source": [ - "# qBraid-SDK Braket on IonQ device Tutorial: Quantum Teleportation " - ] - }, - { - "cell_type": "markdown", - "id": "2185bb76-2ab7-40e7-86e4-f22b7970aa2b", - "metadata": {}, - "source": [ - "Per usual, install the qBraid SDK environment on Lab, and use the qBraid CLI to enable [Quantum Jobs](https://docs.qbraid.com/en/latest/lab/quantumjobs.html)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "485931da-0c44-447d-9c28-49c8bc63f182", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;35mSuccessfully enabled qBraid Quantum Jobs in the \u001b[1;35mqbraid_sdk\u001b[0m\u001b[0;35m environment.\u001b[0m\n", - "\u001b[0;35mEvery \u001b[1;35mAWS\u001b[0m\u001b[0;35m job you run will now be submitted through the qBraid API, so no access keys/tokens are necessary. \u001b[0m\n", - "\n", - "\u001b[0;35mTo disable, run:\u001b[0m `qbraid jobs disable qbraid_sdk`\n" - ] - } - ], - "source": [ - "!qbraid jobs enable qbraid_sdk" - ] - }, - { - "cell_type": "markdown", - "id": "b5b3a0d9-3dba-41c2-bd80-9fae69666ecd", - "metadata": {}, - "source": [ - "You can check that the `jobs` keyword next to the qBraid SDK environment is now green.\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e72b7451-b77f-4f96-8a82-ded77c4df11c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# installed environments:\n", - "#\n", - "default \u001b[0;31mjobs\u001b[0m /opt/.qbraid/environments/qbraid_000000\n", - "aws_braket \u001b[0;32mjobs\u001b[0m /home/jovyan/.qbraid/environments/aws_braket_kwx6dl\n", - "qiskit \u001b[0;31mjobs\u001b[0m /home/jovyan/.qbraid/environments/qiskit_i5o7if\n", - "qiskit_gpu /home/jovyan/.qbraid/environments/qiskit_gpu_tyt64d\n", - "tensorflow /home/jovyan/.qbraid/environments/tensorflow_uorhf3\n", - "qbraid_sdk \u001b[0;32mjobs\u001b[0m /home/jovyan/.qbraid/environments/qbraid_sdk_9j9sjy\n", - "pyquil /home/jovyan/.qbraid/environments/pyquil_i4l3hx\n", - "mitiq /home/jovyan/.qbraid/environments/mitiq_7rq6q3\n", - "\n" - ] - } - ], - "source": [ - "!qbraid envs list" - ] - }, - { - "cell_type": "markdown", - "id": "c1ae7c7f-45b4-4ca7-848a-ea4577d07371", - "metadata": {}, - "source": [ - "It's important to import the qBraid SDK only *after* you have enabled quantum jobs." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b7f29a4c-03b2-4b01-981e-ea8545ce4e65", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.4.5'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import qbraid\n", - "\n", - "qbraid.__version__" - ] - }, - { - "cell_type": "markdown", - "id": "d8db962e-b6c2-414b-9920-dd84d3931e0d", - "metadata": {}, - "source": [ - "# Creating the Circuit" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "feec7302-e977-479d-9159-84b394dca963", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit import QuantumCircuit" - ] - }, - { - "cell_type": "markdown", - "id": "68903de2-fc1d-436a-b38d-656bcfbddc4d", - "metadata": {}, - "source": [ - "The code for this circuit was taken from IBMs Quantum Teleportation tutorial. Check out their [tutorial](https://www.youtube.com/watch?v=mMwovHK2NrE&t) for a more in depth explanation. The idea is that we want to transport a qubit state from one person (Alice) to another (Bob). We utlizie an entangled Bell state to do so." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b3d8cd6b-fd28-4843-956f-6c0202aa9191", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qiskit_circuit = QuantumCircuit(3, 3)\n", - "qiskit_circuit.x(0)\n", - "# Controlled Hadamard gates are not part of the quantum gates supported by the Harmony device\n", - "qiskit_circuit.ch(0, 1)\n", - "qiskit_circuit.ch(0, 1)\n", - "# ----\n", - "qiskit_circuit.barrier()\n", - "qiskit_circuit.h(1)\n", - "qiskit_circuit.cx(1, 2)\n", - "qiskit_circuit.barrier()\n", - "qiskit_circuit.cx(0, 1)\n", - "qiskit_circuit.h(0)\n", - "qiskit_circuit.barrier()\n", - "qiskit_circuit.measure([0, 1], [0, 1])\n", - "qiskit_circuit.barrier()\n", - "qiskit_circuit.cx(1, 2)\n", - "qiskit_circuit.cz(0, 2)\n", - "qiskit_circuit.measure([2], [2])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5870442b-46dd-4d5a-9654-6947cc8a5a1b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
     ┌───┐           ░            ░      ┌───┐ ░ ┌─┐    ░            \n",
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-       "c: 3/═════════════════════════════════════════════╩══╩═════════════╩═\n",
-       "                                                  0  1             2
" - ], - "text/plain": [ - " ┌───┐ ░ ░ ┌───┐ ░ ┌─┐ ░ \n", - "q_0: ┤ X ├──■────■───░────────────░───■──┤ H ├─░─┤M├────░───────■────\n", - " └───┘┌─┴─┐┌─┴─┐ ░ ┌───┐ ░ ┌─┴─┐└───┘ ░ └╥┘┌─┐ ░ │ \n", - "q_1: ─────┤ H ├┤ H ├─░─┤ H ├──■───░─┤ X ├──────░──╫─┤M├─░───■───┼────\n", - " └───┘└───┘ ░ └───┘┌─┴─┐ ░ └───┘ ░ ║ └╥┘ ░ ┌─┴─┐ │ ┌─┐\n", - "q_2: ────────────────░──────┤ X ├─░────────────░──╫──╫──░─┤ X ├─■─┤M├\n", - " ░ └───┘ ░ ░ ║ ║ ░ └───┘ └╥┘\n", - "c: 3/═════════════════════════════════════════════╩══╩═════════════╩═\n", - " 0 1 2 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qbraid.interface import circuit_drawer\n", - "\n", - "circuit_drawer(qiskit_circuit)" - ] - }, - { - "cell_type": "markdown", - "id": "335e23b4-861d-4aa8-a9a8-14f08f273b0c", - "metadata": {}, - "source": [ - "Notice that we've added two controlled-Hadamard gates: applying two controlled-Hadamards back to back is equivalent to the identity gate. Theoretically, this is the same circuit, however controlled-Hadamard gates are not supported on the IonQ computer. We will see how we can leverage qBraid's capabilities to still run this circuit on IonQ's computer." - ] - }, - { - "cell_type": "markdown", - "id": "dad21987-cabe-4b59-b7cc-c8866ed2b644", - "metadata": {}, - "source": [ - "We can observe the supported gate set with the following:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ff80aae8-a99a-45b1-9baa-97f8c613bf46", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Quantum Gates supported by Harmony:\n", - " ['x', 'y', 'z', 'rx', 'ry', 'rz', 'h', 'cnot', 's', 'si', 't', 'ti', 'v', 'vi', 'xx', 'yy', 'zz', 'swap']\n", - "\n" - ] - } - ], - "source": [ - "from braket.aws import AwsDevice\n", - "\n", - "aws_device = AwsDevice(\"arn:aws:braket:us-east-1::device/qpu/ionq/Harmony\")\n", - "\n", - "# show supported quantum operations (supported gates for this device)\n", - "device_operations = aws_device.properties.dict()[\"action\"][\n", - " \"braket.ir.openqasm.program\"\n", - "][\"supportedOperations\"]\n", - "print(f\"Quantum Gates supported by {aws_device.name}:\\n {device_operations}\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "cab57372-ff2c-4b51-84da-fd7b6c7a91cc", - "metadata": {}, - "source": [ - "We initially programmed this circuit in qiskit, but we want to test braket circuit capabilties on the IonQ computer. Thus, we can use qBraid's circuit wrapper to convert the circuit to braket:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c8a4c361-ebec-47d5-b35d-6322c4af558f", - "metadata": {}, - "outputs": [], - "source": [ - "from qbraid import circuit_wrapper" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "597b3f49-da84-4cf5-adf9-77f13107e5eb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "T : |0|1|2|3|4|5|6|7|\n", - " \n", - "q0 : -X-C-C-----C-H-C-\n", - " | | | | \n", - "q1 : ---H-H-H-C-X-C-|-\n", - " | | | \n", - "q2 : ---------X---X-Z-\n", - "\n", - "T : |0|1|2|3|4|5|6|7|\n" - ] - } - ], - "source": [ - "qbraid_circuit = circuit_wrapper(qiskit_circuit)\n", - "braket_circuit = qbraid_circuit.transpile(\"braket\")\n", - "\n", - "circuit_drawer(braket_circuit)" - ] - }, - { - "cell_type": "markdown", - "id": "9b8803e9-e01e-4709-8794-166930701b0c", - "metadata": {}, - "source": [ - "# Attemping to Run the Circuit without qBraid" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "44721c7a-c516-4877-abdb-de7ce18caefb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "An error occurred (ValidationException) when calling the CreateQuantumTask operation (reached max retries: 4): [line 5] arbitrary unitary gates are not supported on the requested device\n" - ] - } - ], - "source": [ - "try:\n", - " job = aws_device.run(braket_circuit, shots=100)\n", - "except Exception as err:\n", - " print(err)" - ] - }, - { - "cell_type": "markdown", - "id": "ed45ca59-8835-4ef7-aa2a-c1e67b1dfa31", - "metadata": {}, - "source": [ - "## Running on an IonQ Device via qBraid" - ] - }, - { - "cell_type": "markdown", - "id": "93ed5968-d3cb-40b3-9db2-6cd102f755f1", - "metadata": {}, - "source": [ - "Let's check which devices (specifically IonQ ones) are online, and also find their device IDs:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "372995f5-fb4d-4835-a685-4e10ef399874", - "metadata": {}, - "outputs": [], - "source": [ - "from qbraid import get_devices" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "48a9c158-0502-4ef6-a6c1-698f98770adb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Supported Devices

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ProviderNameqBraid IDStatus
IonQAria-1aws_ionq_aria1●
IonQHarmonyaws_ionq_harmony●
Device status updated 0 minutes ago
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "get_devices(filters={\"provider\": \"IonQ\"})" - ] - }, - { - "cell_type": "markdown", - "id": "5e5b1c0a-2465-409e-a1ab-476340d63576", - "metadata": {}, - "source": [ - "Let's check how many credits we have left:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "eed2ba9b-40d8-48b9-bbf2-1d5cf1ec7e63", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;35mYou have \u001b[0m955.60\u001b[0;35m remaining qBraid credits.\u001b[0m\n" - ] - } - ], - "source": [ - "!qbraid jobs get-credits" - ] - }, - { - "cell_type": "markdown", - "id": "bfcbbad1-31cd-4a42-863e-bb7565d99571", - "metadata": {}, - "source": [ - "In this tutorial we'll use IonQ's Harmony computer. Now we can use qBraid's [device wrapper](https://docs.qbraid.com/en/latest/sdk/devices.html#device-wrapper) to run a job on an IonQ device. The device wrapper adds a layer of abstraction, allowing us to run a braket circuit on an IonQ quantum computer. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "9909748c-881a-4d18-b430-d90cca1ecc72", - "metadata": {}, - "outputs": [], - "source": [ - "from qbraid import device_wrapper" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "4582374f-33a5-4bbb-bcb7-3772a3a3e967", - "metadata": {}, - "outputs": [], - "source": [ - "ibmq_sim = device_wrapper(\"ibm_q_qasm_simulator\")\n", - "ionq_device = device_wrapper(\"aws_ionq_harmony\")\n", - "\n", - "# # can also use vendor deviceArn / backend ID directly\n", - "# ibmq_sim = device_wrapper(\"ibmq_qasm_simulator\")\n", - "# ionq_device = device_wrapper(\"arn:aws:braket:us-east-1::device/qpu/ionq/Harmony\")" - ] - }, - { - "cell_type": "markdown", - "id": "c429b4af-6668-473d-b15c-ff6cce29dc89", - "metadata": {}, - "source": [ - "Feel free to change the number of shots to reduce the number of qBraid credits:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "2c6b982e-ea85-480f-a44d-5c2f3cc74844", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# running qiskit circuit on AWS device\n", - "ionq_job = ionq_device.run(qiskit_circuit, shots=100)\n", - "ionq_job.status()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "42446731-3b82-423d-9af3-1e8ece783c42", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# comparing against simulator result run on IBM\n", - "ibm_job = ibmq_sim.run(qiskit_circuit, shots=100)\n", - "ibm_job.status()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "f8d12a53-d196-4958-9375-f22b9ce0c428", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'100': 27, '101': 29, '110': 27, '111': 17}\n" - ] - } - ], - "source": [ - "simulator_result = ibm_job.result()\n", - "simulator_counts = simulator_result.measurement_counts()\n", - "print(simulator_counts)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "19d2f2de-43ca-47d3-976b-55c339d25d10", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulator_result.plot_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "f0e06da3-4fe2-4644-a06b-cec1376ab02b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'000': 3, '001': 2, '100': 31, '101': 22, '110': 26, '111': 16}\n" - ] - } - ], - "source": [ - "ionq_result = ionq_job.result()\n", - "ionq_counts = ionq_result.measurement_counts()\n", - "print(ionq_counts)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "535bed02-ca5b-4f5e-ba87-95c451b8a457", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ionq_result.plot_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "58f06d18-c896-4382-ac83-ce8617f5dbd7", - "metadata": {}, - "source": [ - "We see that we've correctly teleported the state, since ``c2`` (the leading qubit) is far more likely to be 1, and we teleported the $|1\\rangle$ state!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 [qBraid]", - "language": "python", - "name": "python3_qbraid_sdk_9j9sjy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/qbraid_sdk/aws_sim_qiskit_bernstein_vazirani.ipynb b/qbraid_sdk/aws_sim_qiskit_bernstein_vazirani.ipynb index 41b82c5..477484c 100644 --- a/qbraid_sdk/aws_sim_qiskit_bernstein_vazirani.ipynb +++ b/qbraid_sdk/aws_sim_qiskit_bernstein_vazirani.ipynb @@ -4,7 +4,9 @@ "cell_type": "code", "execution_count": 1, "id": "e02e4aa7-3ad7-41bd-a23b-5b37b70fdc34", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# This notebook is derived from Qiskit and includes modifications by qBraid.\n", @@ -29,92 +31,21 @@ "# qBraid-SDK Qiskit on AWS Device Demo: Bernstein-Vazirani Algorithm" ] }, - { - "cell_type": "markdown", - "id": "e2ddbf4c-f5bc-487c-8bd1-0f41844b0b74", - "metadata": {}, - "source": [ - "Per usual, install the qBraid SDK environment on Lab, and use the qBraid CLI to enable [Quantum Jobs](https://docs.qbraid.com/en/latest/lab/quantumjobs.html):" - ] - }, { "cell_type": "code", "execution_count": 2, - "id": "3501c116-867a-4917-aee6-f2164bde27a4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;35mSuccessfully enabled qBraid Quantum Jobs in the \u001b[1;35mqbraid_sdk\u001b[0m\u001b[0;35m environment.\u001b[0m\n", - "\u001b[0;35mEvery \u001b[1;35mAWS\u001b[0m\u001b[0;35m job you run will now be submitted through the qBraid API, so no access keys/tokens are necessary. \u001b[0m\n", - "\n", - "\u001b[0;35mTo disable, run:\u001b[0m `qbraid jobs disable qbraid_sdk`\n" - ] - } - ], - "source": [ - "!qbraid jobs enable qbraid_sdk" - ] - }, - { - "cell_type": "markdown", - "id": "737411cd-54ce-4d31-8faa-ddbab41deeda", - "metadata": {}, - "source": [ - "You can check that the `jobs` keyword next to the qBraid SDK environment is now green." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "71c6eb57-99fb-4eaf-b4c3-d31a0200b7d9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# installed environments:\n", - "#\n", - "default \u001b[0;31mjobs\u001b[0m /opt/.qbraid/environments/qbraid_000000\n", - "aws_braket \u001b[0;32mjobs\u001b[0m /home/jovyan/.qbraid/environments/aws_braket_kwx6dl\n", - "qiskit \u001b[0;31mjobs\u001b[0m /home/jovyan/.qbraid/environments/qiskit_i5o7if\n", - "qiskit_gpu /home/jovyan/.qbraid/environments/qiskit_gpu_tyt64d\n", - "tensorflow /home/jovyan/.qbraid/environments/tensorflow_uorhf3\n", - "qbraid_sdk \u001b[0;32mjobs\u001b[0m /home/jovyan/.qbraid/environments/qbraid_sdk_9j9sjy\n", - "pyquil /home/jovyan/.qbraid/environments/pyquil_i4l3hx\n", - "mitiq /home/jovyan/.qbraid/environments/mitiq_7rq6q3\n", - "\n" - ] - } - ], - "source": [ - "!qbraid envs list" - ] - }, - { - "cell_type": "markdown", - "id": "87330d3d-71c3-4013-a959-6b78eb8936d4", - "metadata": {}, - "source": [ - "It's important to import the qBraid SDK only *after* you have enabled quantum jobs." - ] - }, - { - "cell_type": "code", - "execution_count": 4, "id": "26c3e962-ce58-41c4-be95-c41340d8a057", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { "text/plain": [ - "'0.4.5'" + "'0.7.0.dev'" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -135,9 +66,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "45bd059d-461e-40bd-a873-d6ee8a2129b1", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "from qiskit import QuantumCircuit" @@ -148,22 +81,24 @@ "id": "a9c68b61-503a-4112-9d09-76b4b9f48558", "metadata": {}, "source": [ - "The code for this circuit was taken from IBMs Bernstein-Vazirani algorithm tutorial. Check out their [tutorial](https://www.youtube.com/watch?v=sqJIpHYl7oo) for a more in depth explanation. Classically, it takes n queries to decipher a secret string of length n – the Bernstein-Vazirani algorithm allows us to develop an oracle in a quantum circuit that is able to guess the string with just one query!" + "The code for this circuit was taken from IBMs Bernstein-Vazirani algorithm tutorial. Check out their [tutorial](https://www.youtube.com/watch?v=sqJIpHYl7oo) for a more in depth explanation. Classically, it takes $n$ queries to decipher a secret string of length $n$ – the Bernstein-Vazirani algorithm allows us to develop an oracle in a quantum circuit that is able to guess the string with just one query!" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "64112ad2-770c-4142-b338-9f379c99b7e7", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -191,24 +126,60 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "ec22c1b0-c33a-400d-87f7-5079e300698d", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "image/png": 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1adPG6lBgEXIAgOnoB81G+wMA7MjI4hbM1bp1a02aNMnqMGAhcgCA6egHzUb7AwDsKMzqAIBgOnjwoJYvX66DBw9aHQosQg4AMB39oNlofwCAHVHcglFycnI0bdo05eTkWB0KLEIOADAd/aDZaH8AgB1R3AIAAAAAAEDIorgFAAAAAACAkEVxCwAAAAAAACGL4haM4nQ61bVrVzmdTqtDgUXIAQCmox80G+0PALCjCKsDAIKpc+fOWrRokdVhwELkAADT0Q+ajfYHANgRI7cAAAAAAAAQsihuwShZWVlKT09XVlaW1aHAIuQAANPRD5qN9gcA2BHFLRjF4/GovLxcHo/H6lBgEXIAgOnoB81G+wMA7Ig5t4BGKCpcyhxndRS+iQq3OgIAgJ1wLYTpYmJiVFRUFLD9Pfbsv3SouETNXTGafuv4Gq8DISYmJiD7AQBfUdwCGiGHQ4rmuxMAYDCuhTCdw+GQy+UK2P6iop2KKq9UVLRTLperxmsACGXclggAAAAAAICQxd/DYJTOnTtr8eLFateundWhwCLkAADT0Q+ajfYHANgRxS0Yxel0KiUlxeowYCFyAIDp6AfNRvsDAOyI2xJhlNzcXN1///3Kzc21OhRYhBwAYDr6QbPR/gAAO6K4BaMcOHBACxcu1IEDB6wOBRYhBwCYjn7QbLQ/AMCOKG4BAAAAAAAgZFHcAgAAAAAAQMiiuAUAAAAAAICQRXELRgkLC1O/fv0UFkbqm4ocAGA6+kGz0f4AADviqgajuN1urVmzRm632+pQYBFyAIDp6AfNRvsDAOyI4hYAAAAAAABCFsUtAAAAAAAAhCyKWwAAAAAAAAhZFLdglNjYWI0ePVqxsbFWhwKLkAMATEc/aDbaHwBgRxFWBwAEU1JSkubOnWt1GLAQOQDAdPSDZqP9AQB2xMgtGKWsrEy7du1SWVmZ1aHAIuQAANPRD5qN9gcA2BHFLRglOztbI0aMUHZ2ttWhwCLkAADT0Q+ajfYHANgRxS0AAAAAAACELObcAhohj0c6Uml1FL6JCpccDqujAADYBddCAB6PRyUlJVaHUWcxMTFy0AkAlqC4BTRCRyqlmfOtjsI3meOkaHoUAECAcC0EUFJSoqZNm1odRp0VFRXJ5XJZHQZgJG5LBAAAAAAAQMjib0swSlpamrZs2WJ1GLAQOQDAdPSDZqP9AQB2xMgtAAAAAAAAhCyKWzDKjh07NH78eO3YscPqUGARcgCA6egHzUb7AwDsiOIWjFJSUqJNmzaF1FNXEFjkAADT0Q+ajfYHANgRxS0AAAAAAACELIpbAAAAAAAACFkUtwAAAAAAABCyKG7BKImJicrMzFRiYqLVocAi5AAA09EPmo32BwDYUYTVAQDBFBcXpzFjxlgdBixEDgAwHf2g2Wh/AIAdMXILRtm3b59ef/117du3z+pQYBFyAIDp6AfNRvvDVOHh4UpLS9PQoUM1YsQIDRkyRKmpqXI4HHXex6hRo3T33Xc3YJQA/MXILRglLy9Ps2fPVkZGhlq2bGl1OLAAOQDAdPSDZqP9YZKkpCTdfPPNGjZsmDIyMhQTE1NjnUOHDmnDhg1699139eKLL6qgoKDWfY0aNUqLFi1SVFSUwsPD9dhjjzV0+AB8wMgtAAAAAIBtZGRkaNGiRdq5c6f+8Ic/6Lzzzqu1sCVJzZs314UXXqhHH31UOTk5evXVV9W5c+dq6xxf2JKknj17Nvg5APCNEcWtgoICzZgxQ126dJHT6VS7du00depUFRcX68Ybb5TD4dBTTz1ldZhAwOVkfaQnJjq0bum8k67zxESH/j3v0iBGBQRf3gFpwVfSn96V5rwj/fk96d3/SAdKrI4MQEPjWgiYIzIyUn/84x+1Zs0aXXHFFQoPD69a9t133+nNN9/UI488olmzZunRRx/VW2+9pd27d1etEx0drYkTJ+o///mP7rzzTjkcjhqFrddff13XX3990M8NwKnZ/rbEjRs3auTIkcrPz5fL5VK3bt20d+9ePfnkk/ruu++q5hvIyMiwNlAAQMAVlkqvfSZ9m1dz2a4C6YNvpP6dpLH9pIjwmusAAIDQkJCQoKVLl6pXr15V7+Xm5uq5557TCy+8oL179550206dOunWW2/VjTfeqFatWikmJkZPPPGEfv3rX6tr1641CluVlZUNfj4AfGPrkVsFBQUaPXq08vPzdc899ygvL0/r169Xfn6+MjMztXTpUq1Zs0YOh0Pp6elWh4sgcLlcGjhwoFwul9WhwCLkgDkKD0tPvFd7YesYt0f64jvp+Y+kCn5OhSHoB81G+8OOEhMTtWrVqqrCVnl5uWbNmqWOHTvqoYceOmVhS5K+//57zZw5U+3bt9eTTz5Z9X7Pnj0pbAEhwtYjt+68807l5ORoypQpmjev+lD0GTNm6J///Kc2bdqkjh07qnnz5hZFiWBKTk7WCy+8YHUYsBA5YI5XPpUKiuq27rf50jsbpSv6NGhIQKNAP2g22h9207x5c73//vtV82R9//33+uUvf6lNmzb5vK+SkhJNnTpVe/fu1Zw5c6qepPjjjz/qpptuorAFNGK2Hbm1ZcsWzZ8/X61bt9acOXNqXadPH+9vMcdPCLhgwQJdeeWV6tChg2JiYnT22Wfr97//vYqK6vgbEhq1yspKFRUVGXdhqjhSosOFBbV+mcbUHDDN7p+l7370bZvPs6XDRxomHqAxMbUf5FroZWr7w77mzZunbt26SZKys7M1aNAgvwpbx4waNUoPPfRQVWFLks4880zNnDmz3rECaDi2Hbn1xhtvyO12a8KECWratGmt6zRp0kRS9eLWvHnz1L59ez3yyCNKSkrSxo0b9cc//lEff/yxPvnkE4WF2bYeaIStW7dq7NixWrBggdLS0qwOJ2i+WDhLXyycZXUYjYKpOWCaT7f5vs2RCmnNDumCswIfD9CYmNoPci30MrX9YU8XX3yxbr75ZklSYWGhLr744tPegngqJ04ev3z5cg0bNkwRERH6/e9/r7fffrtehTMADce2xa0VK1ZIkoYMGXLSdXJyciRVL2698847OuOMM6peX3jhhTrjjDM0YcIEffrpp7rgggt8jqVv377Kz8/3eTuc3qRJk3xaPy/PO/nOsmXLtGHDhjpt88orr/gYVf2FRzbRFbO3B2x/3YfcopRzrqp12VuPDg/IMVJTUlRZfjgg+/KFXXOgoVzyuzWKiU1QXn6ekpL6WR1Ogxkx/VM1bZ3s83ZPvLhYv/rnHYEPCGhAdu0HuRbWnS854E/7S/a4Fl5xw11yNW1+9BqYVOO13YXi+bvd7pMuczgc1ebHuvfee/X999/7fayTPRVx1qxZeuCBBxQZGam//OUvp/z9MiUlhcEQQD3Ex8dr7dq1fm1r2+LWrl27JEkdOnSodXlFRYVWr14tqXpx6/jC1jF9+/aV5H3ahj/y8/P93hanVlJS4tP6paWlVf/WdVsr2i4iOiag+4uLT1H77sMCus8T7c3bq4oy39ojEOyaAw3l2G0olZWVtjqvEzkinH5tV+EJt/XnAnuyaz/ItbDufMkBf9pfsse10H30Gug+eg088bXd2e38hw0bptTUVEnSp59+queee87vfZ2ssFVZWamHH35Y48aNU2pqqgYPHqxu3bopKyur1v0cKx4DCD7bFreKi4slSYcP1/7Xs/nz56ugoEDNmjVTx44dT7mvlStXSpK6du3qVyzx8fF+bYfTi4nx7Qdfp9NZ9W9dt01MTPQ5rvoKj2wS9GPWV9uEtpb8tdquOdBQwsPDq/6103mdyF3u3y+XEY4KW38usCe79oNcC+vOlxzwp/0le1wLw45eA8OOXgNPfG13oXj+brf7pAWjO+7470jrxx9/3O9jnKqwJXmfvPjUU09VjRK7/fbb9Zvf/KbWfSUkJDByC6iH+tRObFvcio+P1/79+7V+/XoNGDCg2rK8vDxNnz5dkpSenl5tssAT5ebm6oEHHtCIESOUkZHhVyz+DqvD6W3dutWn9Tdv3qyXXnpJl1xySZ3nmZg9e7Y/odVLWYU0c37QD1sv27ZvV7QFPYpdc6ChzFokHTwsJcQnVN2abUdvfil9lu37dvfecpkGzL0s8AEBDciu/SDXwrrzJQf8aX/JHtfCR55+XYeKiquugSe+trtQPP/i4uJa50+Ojo7WqFGjJHl/X1u8eLFf+z9dYeuYf/zjH5ozZ45cLpeuvPLKkxa3tm/fLpfL5VcsAOrHtmXlYcO8w84zMzO1bdt/ZxZes2aNhgwZooIC75NxTlWwKioq0mWXXaaoqCi99NJLDRovgiM1NVWrV6+uGsIM85ADZhjoR/M6I6XeyQEPBWh06AfNRvvDDtLT0xUZGSlJ+uCDD/x6+mddC1uSdPDgQX3++eeSvKOzEhIS6hE9gIZg2+LWjBkz1KpVK+3Zs0dpaWnq0aOHUlJS1L9/f3Xq1ElDhw6VVH2+reMdPnxYo0eP1o4dO/T+++/TgdlEZGSkWrZsWXUxhHnIATMktpDO9rHbHpQqS0ZcAMFGP2g22h920KdPn6r/r1u3zuftfSls1Xac448PoHGwbXErKSlJq1at0qhRo+R0OrVz5061bNlSzz77rJYuXVo1mqu24lZ5ebnGjh2rtWvX6t1331W3bt2CHT4ayO7du3XHHXdo9+7dVocCi5AD5rhuoJQQV7d109tJI9MbNByg0aAfNBvtDzvo0qVL1f+//vprn7b1p7B14nGOPz6AxsHWf6Pu2rWrlixZUuP9oqIi7dy5U2FhYerevXu1ZW63WxMmTND//d//admyZerfv3+wwkUQFBYWauXKlZo8ebLVoQRFUrfBmvqa55TrnG653ZiWAyaLiZZ+M9w7/9amPZKnllSPDJfOT5UuzZCY/xWmMK0f5FpYnWntD3vKzs7Whx9+qCZNmuiHH36o83YDBw70q7AlSXv27NGnn36qw4cPN9o5ygCT2bq4dTKbN2+Wx+NRampqjafETJ48Wf/7v/+r3/72t4qJidE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+ "text/html": [ + "
           ░ ┌───┐ ░                      ░ ┌───┐ ░ ┌─┐               \n",
+       "q_0: ──────░─┤ H ├─░───■──────────────────░─┤ H ├─░─┤M├───────────────\n",
+       "           ░ ├───┤ ░   │                  ░ ├───┤ ░ └╥┘┌─┐            \n",
+       "q_1: ──────░─┤ H ├─░───┼──────────────────░─┤ H ├─░──╫─┤M├────────────\n",
+       "           ░ ├───┤ ░   │                  ░ ├───┤ ░  ║ └╥┘┌─┐         \n",
+       "q_2: ──────░─┤ H ├─░───┼────■─────────────░─┤ H ├─░──╫──╫─┤M├─────────\n",
+       "           ░ ├───┤ ░   │    │             ░ ├───┤ ░  ║  ║ └╥┘┌─┐      \n",
+       "q_3: ──────░─┤ H ├─░───┼────┼─────────────░─┤ H ├─░──╫──╫──╫─┤M├──────\n",
+       "           ░ ├───┤ ░   │    │             ░ ├───┤ ░  ║  ║  ║ └╥┘┌─┐   \n",
+       "q_4: ──────░─┤ H ├─░───┼────┼────■────────░─┤ H ├─░──╫──╫──╫──╫─┤M├───\n",
+       "           ░ ├───┤ ░   │    │    │        ░ ├───┤ ░  ║  ║  ║  ║ └╥┘┌─┐\n",
+       "q_5: ──────░─┤ H ├─░───┼────┼────┼────■───░─┤ H ├─░──╫──╫──╫──╫──╫─┤M├\n",
+       "     ┌───┐ ░ ├───┤ ░ ┌─┴─┐┌─┴─┐┌─┴─┐┌─┴─┐ ░ ├───┤ ░  ║  ║  ║  ║  ║ └╥┘\n",
+       "q_6: ┤ X ├─░─┤ H ├─░─┤ X ├┤ X ├┤ X ├┤ X ├─░─┤ H ├─░──╫──╫──╫──╫──╫──╫─\n",
+       "     └───┘ ░ └───┘ ░ └───┘└───┘└───┘└───┘ ░ └───┘ ░  ║  ║  ║  ║  ║  ║ \n",
+       "c: 6/════════════════════════════════════════════════╩══╩══╩══╩══╩══╩═\n",
+       "                                                     0  1  2  3  4  5
" + ], "text/plain": [ - "
" + " ░ ┌───┐ ░ ░ ┌───┐ ░ ┌─┐ \n", + "q_0: ──────░─┤ H ├─░───■──────────────────░─┤ H ├─░─┤M├───────────────\n", + " ░ ├───┤ ░ │ ░ ├───┤ ░ └╥┘┌─┐ \n", + "q_1: ──────░─┤ H ├─░───┼──────────────────░─┤ H ├─░──╫─┤M├────────────\n", + " ░ ├───┤ ░ │ ░ ├───┤ ░ ║ └╥┘┌─┐ \n", + "q_2: ──────░─┤ H ├─░───┼────■─────────────░─┤ H ├─░──╫──╫─┤M├─────────\n", + " ░ ├───┤ ░ │ │ ░ ├───┤ ░ ║ ║ └╥┘┌─┐ \n", + "q_3: ──────░─┤ H ├─░───┼────┼─────────────░─┤ H ├─░──╫──╫──╫─┤M├──────\n", + " ░ ├───┤ ░ │ │ ░ ├───┤ ░ ║ ║ ║ └╥┘┌─┐ \n", + "q_4: ──────░─┤ H ├─░───┼────┼────■────────░─┤ H ├─░──╫──╫──╫──╫─┤M├───\n", + " ░ ├───┤ ░ │ │ │ ░ ├───┤ ░ ║ ║ ║ ║ └╥┘┌─┐\n", + "q_5: ──────░─┤ H ├─░───┼────┼────┼────■───░─┤ H ├─░──╫──╫──╫──╫──╫─┤M├\n", + " ┌───┐ ░ ├───┤ ░ ┌─┴─┐┌─┴─┐┌─┴─┐┌─┴─┐ ░ ├───┤ ░ ║ ║ ║ ║ ║ └╥┘\n", + "q_6: ┤ X ├─░─┤ H ├─░─┤ X ├┤ X ├┤ X ├┤ X ├─░─┤ H ├─░──╫──╫──╫──╫──╫──╫─\n", + " └───┘ ░ └───┘ ░ └───┘└───┘└───┘└───┘ ░ └───┘ ░ ║ ║ ║ ║ ║ ║ \n", + "c: 6/════════════════════════════════════════════════╩══╩══╩══╩══╩══╩═\n", + " 0 1 2 3 4 5 " ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from qbraid.interface import circuit_drawer\n", + "from qbraid.visualization import circuit_drawer\n", "\n", "circuit_drawer(qiskit_circuit, \"mpl\") # Visualizing the circuit" ] @@ -231,116 +202,46 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "43adf303-4bd1-45cb-bac8-010cbd701e72", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "from qbraid import get_devices" + "from qbraid.runtime.braket import BraketProvider" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "793dc61d-9a59-46ba-b553-8202892e6e7c", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "text/html": [ - "

Supported Devices

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ProviderNameqBraid IDStatus
AWSDM1aws_dm_sim●
AWSSV1aws_sv_sim●
AWSTN1aws_tn_sim●
IonQAria-1aws_ionq_aria1●
IonQHarmonyaws_ionq_harmony●
OQCLucyaws_oqc_lucy○
QuEraAquilaaws_quera_aquila○
RigettiAspen-M-2aws_rigetti_aspen_m2○
RigettiAspen-M-3aws_rigetti_aspen_m3●
XanaduBorealisaws_xanadu_borealis○
Device status updated 0 minutes ago
" - ], "text/plain": [ - "" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] }, + "execution_count": 7, "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "get_devices(filters={\"runPackage\": \"braket\"})" - ] - }, - { - "cell_type": "markdown", - "id": "02a21356-272e-49d4-98f4-a7387f8e68d5", - "metadata": {}, - "source": [ - "While we're at it, let's check how many credits we have left:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d6234c6e-5f98-4730-b969-ea65affe2140", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;35mYou have \u001b[0m956.00\u001b[0;35m remaining qBraid credits.\u001b[0m\n" - ] + "output_type": "execute_result" } ], "source": [ - "!qbraid jobs get-credits" + "provider = BraketProvider()\n", + "provider.get_devices()" ] }, { @@ -348,63 +249,67 @@ "id": "7c96b901-13db-4908-86ed-d3a078e6c4bf", "metadata": {}, "source": [ - "In this tutorial we'll use Amazon's SV1 computer, since we see that it's online. Now we can use qBraid's [device wrapper](https://docs.qbraid.com/en/latest/sdk/devices.html#device-wrapper) to run a job on an Amazon device. The device wrapper adds a layer of abstraction, allowing us to run a qiskit circuit on an AWS quantum computer. Note that there's no need for any sort of circuit wrapper here – we can plug the qiskit circuit directly into the wrapped device!" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "bce1d9cd-0787-49e9-b2bc-8089481c755a", - "metadata": {}, - "outputs": [], - "source": [ - "from qbraid import device_wrapper" + "In this tutorial we'll use Amazon's SV1 computer, since we see that it's online. Now we can use the BraketProvider run a job on an Amazon device. The device wrapper adds a layer of abstraction, allowing us to run a qiskit circuit on an AWS quantum computer. Note that there's no need for any sort of circuit wrapper here – we can plug the qiskit circuit directly into the wrapped device!" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "d0263cb1-f67c-4894-8723-1cf6f0813f7e", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "aws_device_id = \"aws_sv_sim\" # aws_sv_sim is the qBraid id for the SV1 Computer\n", - "device = device_wrapper(aws_device_id)" + "aws_device_id = \"arn:aws:braket:::device/quantum-simulator/amazon/sv1\"\n", + "device = provider.get_device(aws_device_id)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "id": "925f0bbd-ccb2-4806-bb29-299365ee8fa3", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/.qbraid/environments/qbraid_k2j4i1/pyenv/lib/python3.9/site-packages/qiskit_braket_provider/providers/adapter.py:442: UserWarning: The Qiskit circuit contains barrier instructions that are ignored.\n", + " warnings.warn(\n" + ] + }, { "data": { "text/plain": [ "" ] }, - "execution_count": 13, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "braket_job = device.run(qiskit_circuit, shots=1000)\n", - "braket_job.status() # checking the status of our job" + "braket_job = device.run(qiskit_circuit, shots=100)\n", + "braket_job.status()" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "id": "04629aba-806f-43d1-951b-d673b7a3e6e6", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'1110101': 1000}\n" + "{'1110101': 100}\n" ] } ], @@ -416,24 +321,37 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "id": "aaf2c526-33ce-4eae-9602-09148d11026d", - "metadata": {}, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from qbraid.visualization import plot_histogram" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0e571804-2fbe-464e-994d-8911d19c40e0", + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "image/png": 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", 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Wu88TTzyhZ555Rs8//7w2bNigVq1aadSoUaqsrGzAzgEAQGMR0NAN/JAvvvhCY8eO1ZgxYyRJCQkJeuONN7Rx40ZJ1Wd15s6dq/vvv19jx46VJL3yyiuKjo7WkiVLNHny5AbrHQAANA6N+szO+eefr7S0NGVmZkqStm7dqrVr1+qSSy6RJOXk5Cg/P1+pqane+4SGhmrgwIFat27dKY/rcrnkdDpr/QMAAPbUqM/s3HvvvXI6nerWrZscDofcbrf++te/asqUKZKk/Px8SVJ0dHSt+0VHR3u3ncycOXM0e/bs79U3b96s4OBgSVJKSorKysqUnZ3t3f5dH9u3b/fWEhISFBERofT09Frrd+zYURkZGbq+i1uStL/C0McHTI1u71Zcy+r9yqqkt3McGhxlqXuY7zqk+Zmmuod5NCjKV3t3j6lWAdKo9pa3lpZn6kilNDHJV0s/bGhrkalpyW6ZRnUty2nos3xTv+7oVpvA6lphpbR0n0PD4ywlBFev47Kk13c51DfSUu9w39oLs021ayVdHONbZ1muKcsjXd7BV1tbYCinzNC1nX21r4oNbTpk6upObgU5qmv7yg2tyDN1abxbMUHVtdIq6Z0ch4ZEW+oa6lv7pUyHzmljaUBbX+2dPaZaN5NGtvOts+KAqZLj0pWJvtqmQ4a+KjY1vYtbNaNQZqmhtQWmxie4Fda8ulZwTFqW69CIOEsda2ZR6ZYWZjvUL9LSuX6zeH2Xqfhgjy6K8dWW7qv+m+Eyv1msyTeUW25oit8sthUZ2nzY1DWd3GpRM4u95YbS8kyNiXcrumYWJceld/c4dEG0pS41s/BImp/pUK82lvr7zeK/OabCmkupfrP4+ICpsippQoKvtvGQoa+LTe9/j5K0s9TQ5wWmJiS6FdqsupZ/TPpfrkOpcZY61MzimFt6I9uh/m0t9WrjW/vVXaYSW3t0QbSv9sE+U6YhjYn3rb0639SBCumaTr7a1iJD6YdNTensVmDNn1x7yg2tzDN1WQe3olpU14pd0uK9Dl0YYyk5pHodyyMtyHKod7ilvpG+td/abSqihTQizrfOR/tNVZyQxvvNYn2hoR0lhqZ38dV2lBhaV2jqqkS3WtfMIu+otHy/QyPbWWrfqnqdihPSot0ODWxrqaffLF7JMtUpxKMhfrN4b6+p5qZ0id8sVh00lX9Mmuz3O/vlEUNfHjF1bWe3mtXMYneZoVUHTV3Rwa3ImlkccUnv7XXo4hhLnWpmccIjvZLlUEq4pfP8ZrFot6m2LaThfrNYvt9UpVsa19FXW1doaGepoWnJvto3JYbWF5qamOhWcM0sDhw19NF+U6PaW2rXsnqd8irprRyHBkVZ6uH3+LUgy1TXUI8G+z1+LdlrqoVDGu33+LUyz9ShSmmS3yy2HDaUUWTqumS3Amp+abOdhlbnmxrb0a2Imsevw5XS+/scGhprKal19TpVlvTqLof6RFjqE+Fb+83dpmKCpKGxvnU+zDV13JLG+s3i8wJD2U5D1/nNYnuxoQ2HTE1KcqtVzbMlj+V1eyyXpIMHDyo3N9d7u1evXnK5XN4TGpKUnJysoKAgbdu2zVuLj49XbGys91UdSYqMjFRSUlKt5+MfYnj8r/ZtZN58803dddddevLJJ9WzZ09lZGTo9ttv11NPPaWpU6fqiy++0JAhQ5SXl6fY2Fjv/SZOnCjDMLRo0aKTHtflcsnlcnlvO51OxcfHq7S0VCEhIfX6MyTcu6xejwcAQFOz57ExZ+S4TqdToaGhP/r83ajP7Nx111269957vdfe9OrVS3v37tWcOXM0depUxcTESJIKCgpqhZ2CggKlpKSc8riBgYEKDAw8o70DAIDGoVFfs3P06FGZZu0WHQ6HLKv61FpiYqJiYmKUlpbm3e50OrVhwwYNHjz4rPYKAAAap0Z9Zufyyy/XX//6V3Xo0EE9e/bUl19+qaeeekrXX3+9JMkwDN1+++165JFHlJycrMTERM2aNUtxcXEaN25cwzYPAAAahUYddp599lnNmjVLv//971VYWKi4uDjdfPPNeuCBB7z73H333aqoqNBNN92kkpISXXDBBVq+fLlatGjRgJ0DAIDGolFfoHy2/NQLnOqCC5QBAL90DX2BcqO+ZgcAAOB0EXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICtEXYAAICt1SnsbNmyRV999ZX39nvvvadx48bpT3/6k44fP15vzQEAAJyuOoWdm2++WZmZmZKk3bt3a/LkyWrZsqXefvtt3X333fXaIAAAwOmoU9jJzMxUSkqKJOntt9/WRRddpIULF2rBggV655136rM/AACA01KnsOPxeGRZliRpxYoVuvTSSyVJ8fHxOnz4cP11BwAAcJrqFHb69eunRx55RK+++qpWr16tMWPGSJJycnIUHR1drw0CAACcjjqFnaefflpbtmzRjBkz9Oc//1mdO3eWJP33v//V+eefX68NAgAAnI6Autypd+/etd6N9Z0nn3xSAQF1OiQAAMAZUaczO0lJSTpy5Mj36pWVlerSpctpNwUAAFBf6hR29uzZI7fb/b26y+XS/v37T7spAACA+vKzXnN6//33vf/7o48+UmhoqPe22+1WWlqaEhMT6687AACA0/Szws64ceMkSYZhaOrUqbW2NWvWTAkJCfrb3/5Wb80BAACcrp8Vdr77bJ3ExERt2rRJkZGRZ6QpAACA+lKnt07l5OTUdx8AAABnRJ3fJ56Wlqa0tDQVFhZ6z/h856WXXjrtxgAAAOpDncLO7Nmz9dBDD6lfv36KjY2VYRj13RcAAEC9qFPYef7557VgwQJde+219d0PAABAvarT5+wcP36cr4UAAABNQp3Czm9/+1stXLiwvnsBAACod3V6GauyslIvvPCCVqxYoXPPPVfNmjWrtf2pp56ql+Yk6cCBA7rnnnv04Ycf6ujRo+rcubPmz5+vfv36SZI8Ho8efPBBvfjiiyopKdGQIUP03HPPKTk5ud56AAAATVedws62bduUkpIiSfr6669rbavPi5WLi4s1ZMgQDRs2TB9++KHatm2rrKwstWnTxrvPE088oWeeeUYvv/yyEhMTNWvWLI0aNUrffPONWrRoUW+9AACApqlOYefTTz+t7z5O6vHHH1d8fLzmz5/vrfl/HYXH49HcuXN1//33a+zYsZKkV155RdHR0VqyZIkmT558VvoEAACNV52u2Tlb3n//ffXr109XXXWVoqKi1KdPH7344ove7Tk5OcrPz1dqaqq3FhoaqoEDB2rdunWnPK7L5ZLT6az1DwAA2FOdzuwMGzbsB1+uWrlyZZ0b8rd7924999xzmjlzpv70pz9p06ZNuu2229S8eXNNnTpV+fn5kqTo6Oha94uOjvZuO5k5c+Zo9uzZ36tv3rxZwcHBkqSUlBSVlZUpOzvbu71bt25yOBzavn27t5aQkKCIiAilp6fXWr9jx47KyMjQ9V2qvx1+f4Whjw+YGt3erbiW1fuVVUlv5zg0OMpS9zCP9/7zM011D/NoUJSv9u4eU60CpFHtfR/gmJZn6kilNDHJV0s/bGhrkalpyW6ZNf8XZTkNfZZv6tcd3WoTWF0rrJSW7nNoeJylhODqdVyW9Pouh/pGWuod7lt7Ybapdq2ki2N86yzLNWV5pMs7+GprCwzllBm6trOv9lWxoU2HTF3dya0gR3VtX7mhFXmmLo13KyaoulZaJb2T49CQaEtdQ31rv5Tp0DltLA1o66u9s8dU62bSyHa+dVYcMFVyXLoy0VfbdMjQV8Wmpndx67v/WjNLDa0tMDU+wa2w5tW1gmPSslyHRsRZ6lgzi0q3tDDboX6Rls71m8Xru0zFB3t0UYyvtnRf9d8Ml/nNYk2+odxyQ1P8ZrGtyNDmw6au6eRWi5pZ7C03lJZnaky8W9E1syg5Lr27x6ELoi11qZmFR9L8TId6tbHU328W/80xFdZcSvWbxccHTJVVSRMSfLWNhwx9XWx6/3uUpJ2lhj4vMDUh0a3Qmsvu8o9J/8t1KDXOUoeaWRxzS29kO9S/raVebXxrv7rLVGJrjy6I9tU+2GfKNKQx8b61V+ebOlAhXdPJV9taZCj9sKkpnd0KrPmTa0+5oZV5pi7r4FZUzSvQxS5p8V6HLoyxlBxSvY7lkRZkOdQ73FLfSN/ab+02FdFCGhHnW+ej/aYqTkjj/WaxvtDQjhJD07v4ajtKDK0rNHVVoluta2aRd1Ravt+hke0stW9VvU7FCWnRbocGtrXU028Wr2SZ6hTi0RC/Wby311RzU7rEbxarDprKPyZN9vud/fKIoS+PmLq2s1vNamaxu8zQqoOmrujgVmTNLI64pPf2OnRxjKVONbM44ZFeyXIoJdzSeX6zWLTbVNsW0nC/WSzfb6rSLY3r6KutKzS0s9TQtGRf7ZsSQ+sLTU1MdCu4ZhYHjhr6aL+pUe0ttWtZvU55lfRWjkODoiz18Hv8WpBlqmuoR4P9Hr+W7DXVwiGN9nv8Wpln6lClNMlvFlsOG8ooMnVdslsBNb+02U5Dq/NNje3oVkTN49fhSun9fQ4NjbWU1Lp6nSpLenWXQ30iLPWJ8K395m5TMUHS0FjfOh/mmjpuSWP9ZvF5gaFsp6Hr/GaxvdjQhkOmJiW51arm2ZLH8ro9lkvSwYMHlZub673dq1cvuVwuZWZmemvJyckKCgrStm3bvLX4+HjFxsZq48aN3lpkZKSSkpJqPR//EMPj8Xh+fLfa7rjjjlq3q6qqlJGRoa+//lpTp07V3//+9597yJNq3ry5+vXrpy+++MJbu+2227Rp0yatW7dOX3zxhYYMGaK8vDzFxsZ695k4caIMw9CiRYtOelyXyyWXy+W97XQ6FR8fr9LSUoWEhNRL799JuHdZvR4PAICmZs9jY87IcZ1Op0JDQ3/0+btOZ3aefvrpk9b/8pe/qLy8vC6HPKnY2Fj16NGjVq179+565513JEkxMTGSpIKCglphp6CgwHsB9ckEBgYqMDCw3voEAACNV71es/Ob3/ymXr8Xa8iQIdq5c2etWmZmpjp27Cip+mLlmJgYpaWlebc7nU5t2LBBgwcPrrc+AABA01XnLwI9mXXr1tXr273vuOMOnX/++Xr00Uc1ceJEbdy4US+88IJeeOEFSdVvc7/99tv1yCOPKDk52fvW87i4OI0bN67e+gAAAE1XncLO+PHja932eDw6ePCgNm/erFmzZtVLY5LUv39/LV68WPfdd58eeughJSYmau7cuZoyZYp3n7vvvlsVFRW66aabVFJSogsuuEDLly/nM3YAAICkOl6gPH369Fq3TdNU27ZtNXz4cI0cObLemjtbfuoFTnXBBcoAgF+6JnmBsv+H/AEAADRmp3XNTnp6unbs2CFJ6tmzp/r06VMvTQEAANSXOoWdwsJCTZ48WatWrVJYWJgkqaSkRMOGDdObb76ptm3b1mePAAAAdVant57feuutKisr0/bt21VUVKSioiJ9/fXXcjqduu222+q7RwAAgDqr05md5cuXa8WKFerevbu31qNHD82bN69JXqAMAADsq05ndizLUrNmzb5Xb9asmSzLOsk9AAAAGkadws7w4cP1hz/8QXl5ed7agQMHdMcdd2jEiBH11hwAAMDpqlPY+cc//iGn06mEhAR16tRJnTp1UmJiopxOp5599tn67hEAAKDO6nTNTnx8vLZs2aIVK1bo22+/lVT9BZ2pqan12hwAAMDp+llndlauXKkePXrI6XTKMAz96le/0q233qpbb71V/fv3V8+ePfXZZ5+dqV4BAAB+tp8VdubOnasbb7zxpB/JHBoaqptvvllPPfVUvTUHAABwun5W2Nm6datGjx59yu0jR45Uenr6aTcFAABQX35W2CkoKDjpW86/ExAQoEOHDp12UwAAAPXlZ4Wddu3a6euvvz7l9m3btik2Nva0mwIAAKgvPyvsXHrppZo1a5YqKyu/t+3YsWN68MEHddlll9VbcwAAAKfrZ731/P7779e7776rLl26aMaMGeratask6dtvv9W8efPkdrv15z//+Yw0CgAAUBc/K+xER0friy++0O9+9zvdd9998ng8kiTDMDRq1CjNmzdP0dHRZ6RRAACAuvjZHyrYsWNH/e9//1NxcbF27dolj8ej5ORktWnT5kz0BwAAcFrq9AnKktSmTRv179+/PnsBAACod3X6biwAAICmgrADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsrUmFnccee0yGYej222/31iorK3XLLbcoIiJCwcHBmjBhggoKChquSQAA0Kg0mbCzadMm/etf/9K5555bq37HHXfogw8+0Ntvv63Vq1crLy9P48ePb6AuAQBAY9Mkwk55ebmmTJmiF198UW3atPHWS0tL9Z///EdPPfWUhg8frr59+2r+/Pn64osvtH79+gbsGAAANBZNIuzccsstGjNmjFJTU2vV09PTVVVVVaverVs3dejQQevWrTvl8Vwul5xOZ61/AADAngIauoEf8+abb2rLli3atGnT97bl5+erefPmCgsLq1WPjo5Wfn7+KY85Z84czZ49+3v1zZs3Kzg4WJKUkpKisrIyZWdne7d369ZNDodD27dv99YSEhIUERGh9PT0Wut37NhRGRkZur6LW5K0v8LQxwdMjW7vVlzL6v3KqqS3cxwaHGWpe5jHe//5maa6h3k0KMpXe3ePqVYB0qj2lreWlmfqSKU0MclXSz9saGuRqWnJbplGdS3LaeizfFO/7uhWm8DqWmGltHSfQ8PjLCUEV6/jsqTXdznUN9JS73Df2guzTbVrJV0c41tnWa4pyyNd3sFXW1tgKKfM0LWdfbWvig1tOmTq6k5uBTmqa/vKDa3IM3VpvFsxQdW10irpnRyHhkRb6hrqW/ulTIfOaWNpQFtf7Z09plo3k0a2862z4oCpkuPSlYm+2qZDhr4qNjW9i1s1o1BmqaG1BabGJ7gV1ry6VnBMWpbr0Ig4Sx1rZlHplhZmO9Qv0tK5frN4fZep+GCPLorx1Zbuq/6b4TK/WazJN5RbbmiK3yy2FRnafNjUNZ3calEzi73lhtLyTI2Jdyu6ZhYlx6V39zh0QbSlLjWz8Eian+lQrzaW+vvN4r85psKaS6l+s/j4gKmyKmlCgq+28ZChr4tN73+PkrSz1NDnBaYmJLoV2qy6ln9M+l+uQ6lxljrUzOKYW3oj26H+bS31auNb+9VdphJbe3RBtK/2wT5TpiGNifetvTrf1IEK6ZpOvtrWIkPph01N6exWYM2fXHvKDa3MM3VZB7eiWlTXil3S4r0OXRhjKTmkeh3LIy3Icqh3uKW+kb6139ptKqKFNCLOt85H+01VnJDG+81ifaGhHSWGpnfx1XaUGFpXaOqqRLda18wi76i0fL9DI9tZat+qep2KE9Ki3Q4NbGupp98sXsky1SnEoyF+s3hvr6nmpnSJ3yxWHTSVf0ya7Pc7++URQ18eMXVtZ7ea1cxid5mhVQdNXdHBrciaWRxxSe/tdejiGEudamZxwiO9kuVQSril8/xmsWi3qbYtpOF+s1i+31SlWxrX0VdbV2hoZ6mhacm+2jclhtYXmpqY6FZwzSwOHDX00X5To9pbateyep3yKumtHIcGRVnq4ff4tSDLVNdQjwb7PX4t2WuqhUMa7ff4tTLP1KFKaZLfLLYcNpRRZOq6ZLcCan5ps52GVuebGtvRrYiax6/DldL7+xwaGmspqXX1OlWW9Oouh/pEWOoT4Vv7zd2mYoKkobG+dT7MNXXcksb6zeLzAkPZTkPX+c1ie7GhDYdMTUpyq1XNsyWP5XV7LJekgwcPKjc313u7V69ecrlcyszM9NaSk5MVFBSkbdu2eWvx8fGKjY3Vxo0bvbXIyEglJSXVej7+IYbH4/H8+G4NIzc3V/369dMnn3zivVZn6NChSklJ0dy5c7Vw4UJNnz5dLper1v0GDBigYcOG6fHHHz/pcV0uV637OJ1OxcfHq7S0VCEhIfX6MyTcu6xejwcAQFOz57ExZ+S4TqdToaGhP/r83ahfxkpPT1dhYaHOO+88BQQEKCAgQKtXr9YzzzyjgIAARUdH6/jx4yopKal1v4KCAsXExJzyuIGBgQoJCan1DwAA2FOjfhlrxIgR+uqrr2rVpk+frm7duumee+5RfHy8mjVrprS0NE2YMEGStHPnTu3bt0+DBw9uiJYBAEAj06jDTuvWrXXOOefUqrVq1UoRERHe+g033KCZM2cqPDxcISEhuvXWWzV48GANGjSoIVoGAACNTKMOOz/F008/LdM0NWHCBLlcLo0aNUr//Oc/G7otAADQSDTqC5TPlp96gVNdcIEyAOCXjguUAQAAziDCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsLVGHXbmzJmj/v37q3Xr1oqKitK4ceO0c+fOWvtUVlbqlltuUUREhIKDgzVhwgQVFBQ0UMcAAKCxadRhZ/Xq1brlllu0fv16ffLJJ6qqqtLIkSNVUVHh3eeOO+7QBx98oLffflurV69WXl6exo8f34BdAwCAxiSgoRv4IcuXL691e8GCBYqKilJ6erouuugilZaW6j//+Y8WLlyo4cOHS5Lmz5+v7t27a/369Ro0aFBDtA0AABqRRn1m5/9XWloqSQoPD5ckpaenq6qqSqmpqd59unXrpg4dOmjdunWnPI7L5ZLT6az1DwAA2FOjPrPjz7Is3X777RoyZIjOOeccSVJ+fr6aN2+usLCwWvtGR0crPz//lMeaM2eOZs+e/b365s2bFRwcLElKSUlRWVmZsrOzvdu7desmh8Oh7du3e2sJCQmKiIhQenp6rfU7duyojIwMXd/FLUnaX2Ho4wOmRrd3K65l9X5lVdLbOQ4NjrLUPczjvf/8TFPdwzwaFOWrvbvHVKsAaVR7y1tLyzN1pFKamOSrpR82tLXI1LRkt0yjupblNPRZvqlfd3SrTWB1rbBSWrrPoeFxlhKCq9dxWdLruxzqG2mpd7hv7YXZptq1ki6O8a2zLNeU5ZEu7+CrrS0wlFNm6NrOvtpXxYY2HTJ1dSe3ghzVtX3lhlbkmbo03q2YoOpaaZX0To5DQ6ItdQ31rf1SpkPntLE0oK2v9s4eU62bSSPb+dZZccBUyXHpykRfbdMhQ18Vm5rexa2aUSiz1NDaAlPjE9wKa15dKzgmLct1aEScpY41s6h0SwuzHeoXaelcv1m8vstUfLBHF8X4akv3Vf/NcJnfLNbkG8otNzTFbxbbigxtPmzqmk5utaiZxd5yQ2l5psbEuxVdM4uS49K7exy6INpSl5pZeCTNz3SoVxtL/f1m8d8cU2HNpVS/WXx8wFRZlTQhwVfbeMjQ18Wm979HSdpZaujzAlMTEt0KbVZdyz8m/S/XodQ4Sx1qZnHMLb2R7VD/tpZ6tfGt/eouU4mtPbog2lf7YJ8p05DGxPvWXp1v6kCFdE0nX21rkaH0w6amdHYrsOZPrj3lhlbmmbqsg1tRLaprxS5p8V6HLoyxlBxSvY7lkRZkOdQ73FLfSN/ab+02FdFCGhHnW+ej/aYqTkjj/WaxvtDQjhJD07v4ajtKDK0rNHVVoluta2aRd1Ravt+hke0stW9VvU7FCWnRbocGtrXU028Wr2SZ6hTi0RC/Wby311RzU7rEbxarDprKPyZN9vud/fKIoS+PmLq2s1vNamaxu8zQqoOmrujgVmTNLI64pPf2OnRxjKVONbM44ZFeyXIoJdzSeX6zWLTbVNsW0nC/WSzfb6rSLY3r6KutKzS0s9TQtGRf7ZsSQ+sLTU1MdCu4ZhYHjhr6aL+pUe0ttWtZvU55lfRWjkODoiz18Hv8WpBlqmuoR4P9Hr+W7DXVwiGN9nv8Wpln6lClNMlvFlsOG8ooMnVdslsBNb+02U5Dq/NNje3oVkTN49fhSun9fQ4NjbWU1Lp6nSpLenWXQ30iLPWJ8K395m5TMUHS0FjfOh/mmjpuSWP9ZvF5gaFsp6Hr/GaxvdjQhkOmJiW51arm2ZLH8ro9lkvSwYMHlZub673dq1cvuVwuZWZmemvJyckKCgrStm3bvLX4+HjFxsZq48aN3lpkZKSSkpJqPR//EMPj8Xh+fLeG97vf/U4ffvih1q5dq/bt20uSFi5cqOnTp8vlctXad8CAARo2bJgef/zxkx7L5XLVuo/T6VR8fLxKS0sVEhJSr30n3LusXo8HAEBTs+exMWfkuE6nU6GhoT/6/N0kzuzMmDFDS5cu1Zo1a7xBR5JiYmJ0/PhxlZSU1Dq7U1BQoJiYmFMeLzAwUIGBgWeyZQAA0Eg06mt2PB6PZsyYocWLF2vlypVKTEystb1v375q1qyZ0tLSvLWdO3dq3759Gjx48NluFwAANEKN+szOLbfcooULF+q9995T69atvdfhhIaGKigoSKGhobrhhhs0c+ZMhYeHKyQkRLfeeqsGDx7MO7EAAICkRh52nnvuOUnS0KFDa9Xnz5+vadOmSZKefvppmaapCRMmyOVyadSoUfrnP/95ljsFAACNVaMOOz/l2ukWLVpo3rx5mjdv3lnoCAAANDWN+podAACA00XYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtmabsDNv3jwlJCSoRYsWGjhwoDZu3NjQLQEAgEbAFmFn0aJFmjlzph588EFt2bJFvXv31qhRo1RYWNjQrQEAgAZmi7Dz1FNP6cYbb9T06dPVo0cPPf/882rZsqVeeumlhm4NAAA0sICGbuB0HT9+XOnp6brvvvu8NdM0lZqaqnXr1p30Pi6XSy6Xy3u7tLRUkuR0Ouu9P8t1tN6PCQBAU3Imnl/9j+vxeH5wvyYfdg4fPiy3263o6Oha9ejoaH377bcnvc+cOXM0e/bs79Xj4+PPSI8AAPyShc49s8cvKytTaGjoKbc3+bBTF/fdd59mzpzpvW1ZloqKihQRESHDMBqwMwD1zel0Kj4+Xrm5uQoJCWnodgDUI4/Ho7KyMsXFxf3gfk0+7ERGRsrhcKigoKBWvaCgQDExMSe9T2BgoAIDA2vVwsLCzlSLABqBkJAQwg5gQz90Ruc7Tf4C5ebNm6tv375KS0vz1izLUlpamgYPHtyAnQEAgMagyZ/ZkaSZM2dq6tSp6tevnwYMGKC5c+eqoqJC06dPb+jWAABAA7NF2Jk0aZIOHTqkBx54QPn5+UpJSdHy5cu/d9EygF+ewMBAPfjgg9976RrAL4fh+bH3awEAADRhTf6aHQAAgB9C2AEAALZG2AEAALZG2AEAALZG2AEAALZG2AEAALZG2AEAALZG2AGAk9i3b5/WrFmjqqqqhm4FwGki7ADA/2fnzp3q2rWrrrnmGn3++edyu90N3RKA08AnKAOAn6KiIk2ZMkXR0dHKycnR7t279fLLL+viiy+Ww+Fo6PYA1AFndgDAz6FDh9S1a1dNmzZNq1evVpcuXTR16lStXr2aMzxAE8WZHQDwc+LECWVlZalr164yzeq/B0eMGKHMzEwtWLBAQ4cOlcPhkNvtlmVZatasWQN3DODHEHYA/OJ5PB4ZhvG928ePH1fz5s0l+QLPyy+/rEGDBunJJ59Us2bNdN9999W6L4DGh7AD4Bdt586dWrBggfbu3asLL7xQQ4YM0bnnnusNPCdOnFBAQICk6sCze/du9ejRQx999JEyMjJ0zjnnNPBPAODHEHYA/GJ98803Ov/88zVy5EgdPnxYlZWV2rNnj/71r3/p8ssv9wYet9vtvTg5LCxMAQEBWrFihVJSUhr2BwDwkwQ0dAMA0BAsy9KTTz6pSy65RG+88YYk6euvv9bzzz+vsWPH6q233tKVV14pj8cjh8OhyspKzZw5U5WVlUpPT1fPnj0b+CcA8FMRdgD8IlmWpX379ql3797e2jnnnKOHHnpIzZs315QpUxQREaFhw4ZJkpxOp4qLi7VmzRqCDtDE8DIWgF+sP/7xj1q9erWWLVumqKgobz0vL0933XWXDh8+rEWLFiksLEyS5HK5FBgY2EDdAqgrPmcHwC+KZVne/33RRRfJ7XZr/vz5Kikp8dbj4uJ02WWXaevWrSouLvbWCTpA00TYAfCL4HQ6JUmmaXo/HPCKK67QsGHD9J///EevvPKKDh065N2/X79+CgkJUXl5eYP0C6D+EHYA2N6OHTv0q1/9SnPnzpUkORwOHT9+XJL0t7/9TSNGjNALL7yg2bNn65tvvlFhYaH+/e9/y7IsxcbGNmDnAOoD1+wAsLW9e/dqzJgxOnLkiJKTkzVp0iTdcsstklTrQwMff/xxffTRR1q1apVSUlKUn5+vZcuWqU+fPg3ZPoB6QNgBYFuWZenhhx/Whg0bdNttt+m///2vtm/frt/85jfewON/0XFxcbEyMjLUsmVLtW/fXu3atWvI9gHUE8IOAFvbs2ePPv30U02fPl179+7Vww8//L3AU1VVxXdcATZG2AHwi7J79249+uij2r59u6699lr9/ve/lyQtX75co0ePbuDuAJwJhB0AtlJYWKiCggIdO3ZMAwYM8Na/e6gzDEPZ2dmaM2eOtm/frilTpigrK0vPPvusDh48qKioKL7YE7AZwg4A29i6dauuuuoqnThxQkePHlW7du3097//Xeedd55atmwpj8cjj8cj0zS1e/duzZkzR6+99pqCgoL0ySefqG/fvg39IwA4A3jrOQBbyM/P1/jx43XVVVdpyZIlWrp0qcLDwzVx4kS98cYbKisrk2EYMk1THo9HSUlJOnbsmAIDA/XZZ58RdAAb47uxANjCgQMHZJqmrrvuOnXt2lWS9Mknn+j666/XQw89pJYtW2ry5MkyDEOGYehvf/ubFi5cqC1btvBdV4DNcWYHgC2UlZWppKTE+66qo0ePSpJeeuklDRkyRHfeeacOHz7s3X/KlCn69ttvlZKS0hDtAjiLuGYHgC14PB716tVLycnJWrx4saTan6HTo0cPpaam6plnntGJEycUEMCJbeCXgjM7AJo8y7JkGIYee+wxZWRk6A9/+IOk6i/u/O5rIXr37q3S0lJJIugAvzCEHQBNTmZmpj7++GNJ8r67SpIuvPBCzZgxQ8uXL9dNN90kSd6vg5Cqw49lWeKENvDLwstYAJqUrKwsnXvuuXK5XHrrrbd05ZVX1voMnaKiIi1atEiPPvqo2rZtq0GDBsnpdGrx4sXauHEjFyMDv0CEHQBNRklJiW688UZ5PB7FxcVp3rx5euONNzRx4sRagef48ePav3+/Hn74YVVUVCgoKEh33XWXzjnnnAb+CQA0BF64BtBkFBUVqV27dkpNTdXQoUPVqlUrXX311fJ4PJo0aZIsy5JU/dJVUlKS5s+fL0lyu91yOBwN2TqABkTYAdBkJCUlacaMGercubMk6b777pPH49E111wjj8ejyZMny+PxyO12q7S0VOHh4ZLkvaYHwC8TYQdAk2BZlkzT9AYdSQoJCdH9998vqfpzcwzD0KRJk3TPPfeobdu2uvPOOxUQEMB3XQG/cIQdAI1WYWGhmjdvrrCwsFOenQkODvYGnqlTp+pf//qXVq1apS+//JK3mAOQxFvPATRSO3bsUHx8vG688UY5nc4f3Dc4OFh33XWXkpKStHXrVmVkZKh3795nqVMAjR1hB0CjU1BQoN/+9re64IILtGrVKv32t7/9wcBjWZb++te/6ttvv9WqVat07rnnnsVuATR2hB0Ajc6XX36phIQEPf7441q2bJnS0tJ+MPDk5eXp2LFj2rJli3r16nWWuwXQ2PE5OwAanUOHDmn79u0aOnSoJGn9+vUaM2aMRowYoRdffFGhoaGSqj89+buLj48dO6agoKCGahlAI0bYAdAonOqzcL57F9aGDRt06aWXasSIEfr3v/+toKAg/fvf/1ZSUpJGjRrVAB0DaCoIOwAaXGZmpj744ANdc801io2NPeV+Gzdu1CWXXKLU1FS1atVKr732mnbs2KFOnTqdxW4BNDW8LxNAg9q1a5cGDx6s4uJiHTlyRDNnzlRkZORJ9x0wYIDef/99XXjhhWrTpo3Wr19P0AHwowg7ABpMRUWF5syZoyuuuEL9+/fXjBkzdOLECd19990nDTzHjx/Xa6+9puDgYH322Wfq0aNHA3QNoKkh7ABoMKZpqm/fvoqIiNCkSZMUGRmpyZMnS9JJA8/WrVv12WefKS0tjaAD4Cfjmh0ADaqiokKtWrXy3l60aJGuvvpq3Xnnnbr33nsVEREhy7J04MABxcfHq7i4WG3atGnAjgE0NZzZAdCgvgs6brdbpmlq0qRJ3i/3NAxDt99+u/7v//5POTk5WrhwIUEHwM/GmR0AjYbH45HH45Fpmlq0aJGuvfZaJSUlKTs7Wxs3blSfPn0aukUATRBhB0Cj8t1DkmEYGjFihDIyMrRq1So+GRlAnfEyFoBGxTAMud1u3XXXXfr000+VkZFB0AFwWvhuLACNUs+ePbVlyxa+1BPAaeNlLACNkv/3XgHA6eDMDoBGiaADoL4QdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK39P2LuEBRDujJiAAAAAElFTkSuQmCC", "text/plain": [ - "
" + "
" ] }, - "execution_count": 15, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "result.plot_counts()" + "plot_histogram(counts)" ] }, { @@ -441,42 +359,15 @@ "id": "8b304a4c-1e71-4438-bd46-12be95c39ae1", "metadata": {}, "source": [ - "We have guessed the number correctly! Note the bar graph where only one number was guessed in all 1000 shots" - ] - }, - { - "cell_type": "markdown", - "id": "e1c1e1b4-bb65-4652-9cf4-9f871f4c64e2", - "metadata": {}, - "source": [ - "Finally we can estimate the cost of our job:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b2a2078f-6529-4584-a70e-6608d67c3ea2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "costEstimate: 0.40009555 credits ~ $0.0040009555\n" - ] - } - ], - "source": [ - "cost = braket_job.metadata()[\"cost\"]\n", - "print(f\"costEstimate: {cost} credits ~ ${cost/100}\")" + "We have guessed the number correctly! Note the bar graph where only one number was guessed in all 100 shots" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 [qBraid]", + "display_name": "runtime", "language": "python", - "name": "python3_qbraid_sdk_9j9sjy" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -488,7 +379,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/qbraid_sdk/ibm_batch_jobs_grovers.ipynb b/qbraid_sdk/ibm_batch_jobs_grovers.ipynb index 57607fc..c00b28e 100644 --- a/qbraid_sdk/ibm_batch_jobs_grovers.ipynb +++ b/qbraid_sdk/ibm_batch_jobs_grovers.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ac6f7b61-1fa3-476d-b6ce-48b1237e8df5", "metadata": {}, "outputs": [], @@ -10,7 +10,7 @@ "# This notebook is derived from Qiskit and includes modifications by qBraid.\n", "#\n", "# (C) Copyright IBM 2020.\n", - "# (C) Copyright qBraid 2023.\n", + "# (C) Copyright qBraid 2024.\n", "#\n", "# This code is licensed under the Apache License, Version 2.0. You may\n", "# obtain a copy of this license in the LICENSE.txt file in the root directory\n", @@ -31,17 +31,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "c58a6923-26f6-4d27-8a6e-32323e4484d1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0.4.5'" + "'0.7.0.dev20240516020308'" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "984a0746-808a-4d06-9adb-2d7513dead36", "metadata": {}, "outputs": [], @@ -77,12 +77,12 @@ "id": "7b97a410-48ff-4b68-96d0-1e0e63de92ae", "metadata": {}, "source": [ - "The code for this circuit was taken from IBMs [Qiskit Textbook](https://learn.qiskit.org/course/ch-algorithms/grovers-algorithm). Grover's algorithm let's us find a marked item in a box in √N steps as opposed to N steps classicaly. In this case we'll run Grover's algorithm for various numbers of steps to observe how the performance varies. This will allow us to test qBraid's [run_batch](https://github.com/qBraid/qBraid/blob/6e6cecc3ec4b7bac973f557606778d2cd07b8307/qbraid/devices/ibm/device.py#L111) functionality." + "The code for this circuit was taken from IBMs [Qiskit Textbook](https://learn.qiskit.org/course/ch-algorithms/grovers-algorithm). Grover's algorithm let's us find a marked item in a box in √N steps as opposed to N steps classicaly. In this case we'll run Grover's algorithm for various numbers of steps to observe how the performance varies." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "1eba1979-e8c7-4388-aa3a-29e3a1ff7cef", "metadata": {}, "outputs": [ @@ -90,7 +90,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "marked entry: 1\n" + "marked entry: 3\n" ] } ], @@ -102,23 +102,31 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "17c2cef0-5746-4558-a3af-51890b7ebd8c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "Vf = QuantumCircuit(n, n)\n", + "# Circuit from qiskit textbook\n", + "def initialize_s(qc, qubits):\n", + " \"\"\"Apply a H-gate to 'qubits' in qc\"\"\"\n", + " for q in qubits:\n", + " qc.h(q)\n", + " return qc\n", "\n", - "Uw = []\n", - "for i in range(2**n):\n", - " if i == marked_entry:\n", - " Uw.append(-1)\n", - " else:\n", - " Uw.append(1)\n", - "Vf.diagonal(Uw, [0, 1, 2])\n", "\n", - "\n", - "# Diffuser from the Qiskit Textbook\n", "def diffuser(nqubits):\n", " qc = QuantumCircuit(nqubits)\n", " # Apply transformation |s> -> |00..0> (H-gates)\n", @@ -129,7 +137,7 @@ " qc.x(qubit)\n", " # Do multi-controlled-Z gate\n", " qc.h(nqubits - 1)\n", - " qc.mct(list(range(nqubits - 1)), nqubits - 1) # multi-controlled-toffoli\n", + " qc.mcx(list(range(nqubits - 1)), nqubits - 1) # multi-controlled-toffoli\n", " qc.h(nqubits - 1)\n", " # Apply transformation |11..1> -> |00..0>\n", " for qubit in range(nqubits):\n", @@ -143,25 +151,34 @@ " return U_s\n", "\n", "\n", - "W = QuantumCircuit(n, n)\n", - "W.append(diffuser(n), range(n))\n", - "VW = Vf.compose(W)" + "qc = QuantumCircuit(3)\n", + "qc.cz(0, 2)\n", + "qc.cz(1, 2)\n", + "oracle_ex3 = qc.to_gate()\n", + "oracle_ex3.name = \"U$_\\omega$\"\n", + "\n", + "grover_circuit = QuantumCircuit(n)\n", + "\n", + "grover_circuit.append(oracle_ex3, [0, 1, 2])\n", + "grover_circuit.append(diffuser(n), [0, 1, 2])\n", + "\n", + "grover_circuit.draw(\"mpl\")" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "2cc8b575-f61f-456c-b185-340a909aa6fe", "metadata": {}, "outputs": [], "source": [ "# create list of circuits to run\n", "n_steps = 3\n", - "grover_init = QuantumCircuit(VW.num_qubits, VW.num_clbits)\n", + "grover_init = QuantumCircuit(grover_circuit.num_qubits, grover_circuit.num_qubits)\n", "grover_init.h(range(n)) # add the Hadamards\n", "circuits = [grover_init] # circuits[j] will have (VW)**j:\n", "for _ in range(n_steps):\n", - " circuits.append(circuits[-1].compose(VW))\n", + " circuits.append(circuits[-1].compose(grover_circuit))\n", "for grover in circuits:\n", " grover.measure(range(n), range(n)) # add measurements" ] @@ -174,9 +191,9 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "
" ] }, "execution_count": 7, @@ -185,9 +202,9 @@ } ], "source": [ - "from qbraid.interface import circuit_drawer\n", + "from qbraid.visualization import circuit_drawer\n", "\n", - "circuit_drawer(circuits[3], \"mpl\")" + "circuit_drawer(circuits[3], output=\"mpl\")" ] }, { @@ -208,7 +225,15 @@ }, { "cell_type": "markdown", - "id": "0346ff12-5c3f-4c59-86f9-1e150b9b4c26", + "id": "ac24da6b", + "metadata": {}, + "source": [ + "Now let's load in our IBM account. Note that you'll have to have IBM credentials to run this notebook. You can follow the instructions [here](https://github.com/Qiskit/qiskit-ibm-provider#provider-setup) to set them up." + ] + }, + { + "cell_type": "markdown", + "id": "653f5850", "metadata": {}, "source": [ "Check which devices are online:" @@ -222,117 +247,23 @@ "outputs": [ { "data": { - "text/html": [ - "

Supported Devices

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ProviderNameqBraid IDStatus
IBMBelemibm_q_belem●
IBMExt. stabilizer simulatoribm_q_simulator_extended_stabilizer●
IBMJakartaibm_q_jakarta●
IBMLagosibm_q_lagos●
IBMLimaibm_q_lima○
IBMMPS simulatoribm_q_simulator_mps●
IBMManilaibm_q_manila●
IBMNairobiibm_q_nairobi○
IBMOsloibm_q_oslo○
IBMPerthibm_q_perth●
IBMQASM simulatoribm_q_qasm_simulator●
IBMQuitoibm_q_quito●
IBMStabilizer simulatoribm_q_simulator_stabilizer●
IBMState vector simulatoribm_q_simulator_statevector●
Device status updated 0 minutes ago
" - ], "text/plain": [ - "" + "[,\n", + " ,\n", + " ,\n", + " ]" ] }, + "execution_count": 8, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "from qbraid import get_devices\n", - "\n", - "get_devices(filters={\"vendor\": \"IBM\"})" - ] - }, - { - "cell_type": "markdown", - "id": "0a7f5c71-231d-4485-a5f1-2373efcb7c9b", - "metadata": {}, - "source": [ - "Now let's load in our IBM account. Note that you'll have to have IBM credentials to run this notebook. You can follow the instructions [here](https://github.com/Qiskit/qiskit-ibm-provider#provider-setup) to set them up." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e3db73c0-b22a-41ec-a49b-cfcfc200feb8", - "metadata": {}, - "outputs": [], - "source": [ - "# from qiskit_ibm_provider import IBMProvider\n", + "from qbraid.runtime.qiskit import QiskitRuntimeProvider\n", "\n", - "# # Replace with your IBM Credentials\n", - "# IBMProvider.save_account(token='MY_API_TOKEN')" + "provider = QiskitRuntimeProvider(\"YOUR_API_KEY\")\n", + "provider.get_devices()" ] }, { @@ -340,7 +271,7 @@ "id": "4e4f1c13-9949-4d6f-a887-b87afbb95407", "metadata": {}, "source": [ - "In this tutorial we'll use IBM's Lima computer, since we see that it's online. Now we can use qBraid's [device wrapper](https://docs.qbraid.com/en/latest/sdk/devices.html#device-wrapper) to run this job. The device wrapper adds a layer of abstraction, allowing us to run more types of circuits on more devices, and in this case letting us use the `run_batch` method. " + "In this tutorial we'll use IBM's Osaka computer, since we see that it's online. Now we can use qBraid's `QuantumDevice` to run this job. The device wrapper adds a layer of abstraction, allowing us to run more types of circuits on more devices, and in this case letting us use the `run_batch` method. " ] }, { @@ -353,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "31c1b440-6c79-4b97-b7a6-9a0aa6703c4d", "metadata": {}, "outputs": [ @@ -361,34 +292,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "Quito\n" + "QiskitBackend('ibm_osaka')\n" ] } ], "source": [ - "from qbraid import device_wrapper\n", - "from qbraid.devices.ibm import ibm_least_busy_qpu\n", + "device = provider.get_device(\"ibm_osaka\")\n", "\n", - "least_busy = ibm_least_busy_qpu()\n", - "\n", - "device = device_wrapper(least_busy)\n", - "\n", - "print(device.name)" + "print(device)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "1ba007b5-ae3c-4223-92ee-e2fe5ee2653e", "metadata": {}, "outputs": [], "source": [ - "job = device.run_batch(circuits, shots=1000)" + "job = device.run(circuits, shots=100)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "8e5895a1-c977-4e8a-bd5a-e365a66d17c5", "metadata": {}, "outputs": [ @@ -398,7 +324,7 @@ "" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -407,9 +333,17 @@ "job.status()" ] }, + { + "cell_type": "markdown", + "id": "a85c3e7d", + "metadata": {}, + "source": [ + "Since it takes awhile to run, we grab the id directly after it finishes." + ] + }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "aaf0e4fa-f163-4b9b-9b25-629c44424d4b", "metadata": {}, "outputs": [ @@ -419,22 +353,22 @@ "" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from qbraid import job_wrapper\n", + "from qbraid.runtime.qiskit import QiskitJob\n", "\n", - "job = job_wrapper(\"ibm_q_quito-ryanjh88-qjob-tpiweqahzl7g43udq1j9\")\n", + "job = QiskitJob(\"cs71v95yhpyg008b1kcg\", device=device)\n", "\n", "job.status()" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "23bab44e-ab8f-443a-a9ec-d23c2b244345", "metadata": {}, "outputs": [ @@ -442,10 +376,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'000': 157, '001': 127, '010': 122, '011': 114, '100': 141, '101': 122, '110': 105, '111': 112}\n", - "{'000': 95, '001': 582, '010': 112, '011': 77, '100': 31, '101': 57, '110': 25, '111': 21}\n", - "{'000': 145, '001': 470, '010': 60, '011': 103, '100': 53, '101': 71, '110': 34, '111': 64}\n", - "{'000': 161, '001': 221, '010': 118, '011': 95, '100': 119, '101': 113, '110': 98, '111': 75}\n" + "{'000': 12, '001': 11, '010': 8, '011': 15, '100': 13, '101': 13, '110': 16, '111': 12}\n", + "{'000': 8, '001': 3, '010': 7, '011': 1, '100': 6, '101': 29, '110': 37, '111': 9}\n", + "{'000': 6, '001': 6, '010': 13, '011': 7, '100': 21, '101': 17, '110': 14, '111': 16}\n", + "{'000': 23, '001': 11, '010': 17, '011': 3, '100': 8, '101': 6, '110': 17, '111': 15}\n" ] } ], @@ -462,15 +396,15 @@ "id": "ea1ad431-980f-4b8e-a131-cd923e3f5163", "metadata": {}, "source": [ - "We see that our results line up roughly with the theoretical prediction in the xtebook. With 0 grover steps, the probability is basically evenly distributed. At one step, we see it is roughly 80% correct, as we expect. The probability then peaks at 2 steps and dips again at 3 steps." + "We see that our results line up roughly with the theoretical prediction in the textbook. With 0 grover steps, the probability is basically evenly distributed. At one step, we see it is roughly 80% correct, as we expect. The probability then peaks at 2 steps and dips again at 3 steps." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 [qBraid]", + "display_name": "notebooks", "language": "python", - "name": "python3_qbraid_sdk_9j9sjy" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -482,7 +416,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/qbraid_sdk/ibm_quantum_jobs_with_runtime.ipynb b/qbraid_sdk/ibm_quantum_jobs_with_runtime.ipynb old mode 100644 new mode 100755 index 6f845b2..641dbef --- a/qbraid_sdk/ibm_quantum_jobs_with_runtime.ipynb +++ b/qbraid_sdk/ibm_quantum_jobs_with_runtime.ipynb @@ -9,6 +9,8 @@ "\n", "Authors: Sophy Shin, James Weaver, Brian Ingmanson\n", "\n", + "Updated by: Rohan Jain\n", + "\n", "This tutorial is about using Qiskit Runtime through qBraid Quantum Lab. The `Default` Environment supports most of the up-to-date versions of Qiskit, so the first step will be to bring your own credential from [IBM Quantum Platform](https://quantum.ibm.com/). \n", "\n", "If you do not already have a user account, get one at the [IBM Quantum login page](https://quantum.ibm.com/login). Your user account is associated with one or more [instances](https://docs.quantum.ibm.com/run/instances) (in the form hub / group / project) that give access to IBM Quantum services. Additionally, a unique token is assigned to each account, allowing for IBM Quantum access from Qiskit. The instructions in this section use our default instance. For instructions to choose a specific instance, see [Connect to an instance](https://docs.quantum.ibm.com/run/instances#connect-instance).\n", @@ -26,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "797cf94f-512c-4260-a8de-16246d468467", "metadata": { "tags": [] @@ -52,12 +54,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "6fc011cc-77df-461a-bc99-93ffd22eb672", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "service.backends()" ] @@ -67,19 +83,19 @@ "id": "0f8ba878-89b0-458e-8854-034cb7c7b433", "metadata": {}, "source": [ - "The `QiskitRuntimeService.backend()` method (note that this is singular: backend) takes the name of the backend as the input parameter and returns an IBMBackend instance representing that particular backend. The following code will select `ibmq_qasm_simulator` and save it as a `backend_sim`" + "The `QiskitRuntimeService.backend()` method (note that this is singular: backend) takes the name of the backend as the input parameter and returns an IBMBackend instance representing that particular backend. The following code will select `ibm_kyoto` and save it as a `backend_sim`" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "f6bf1f98-b992-47fc-85ea-ff534b7977eb", "metadata": { "tags": [] }, "outputs": [], "source": [ - "backend_sim = service.backend(\"ibmq_qasm_simulator\")" + "backend_sim = service.backend(\"ibm_kyoto\")" ] }, { @@ -94,12 +110,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "67109b11-1157-4356-9e24-5a63bb513795", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "backend = service.least_busy(simulator=False, operational=True)\n", "backend" @@ -112,22 +139,52 @@ "source": [ "### Create a toy circuit\n", "\n", - "Now, let's create a random circuit by using `qiskit.circuit.random.random_circuit` with 5 qubits with depth=3 with measurement. " + "Now, let's create a simple bell state using qiskit;" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "e7610fbe-d2b0-4475-9c7c-e6e0ec751d66", "metadata": { "tags": [] }, - "outputs": [], - "source": [ - "from qiskit.circuit.random import random_circuit\n", + "outputs": [ + { + "data": { + "text/html": [ + "
        ┌───┐      ░ ┌─┐   \n",
+       "   q_0: ┤ H ├──■───░─┤M├───\n",
+       "        └───┘┌─┴─┐ ░ └╥┘┌─┐\n",
+       "   q_1: ─────┤ X ├─░──╫─┤M├\n",
+       "             └───┘ ░  ║ └╥┘\n",
+       "meas: 2/══════════════╩══╩═\n",
+       "                      0  1
" + ], + "text/plain": [ + " ┌───┐ ░ ┌─┐ \n", + " q_0: ┤ H ├──■───░─┤M├───\n", + " └───┘┌─┴─┐ ░ └╥┘┌─┐\n", + " q_1: ─────┤ X ├─░──╫─┤M├\n", + " └───┘ ░ ║ └╥┘\n", + "meas: 2/══════════════╩══╩═\n", + " 0 1 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit\n", "\n", - "circ = random_circuit(5, 3, measure=True)\n", - "circ.draw(output=\"mpl\", style=\"iqp\")" + "circ = QuantumCircuit(2)\n", + "circ.h(0)\n", + "circ.cx(0, 1)\n", + "circ.measure_all()\n", + "\n", + "circ.draw()" ] }, { @@ -137,29 +194,46 @@ "source": [ "### Execute using a quantum primitive function\n", "\n", - "Quantum computers can produce random results, so you'll often want to collect a sample of the outputs by running the circuit many times. You can use the `Sampler` class to get measured data from a quantum Computer.. `Sampler` is one of our two [primitives](https://docs.quantum.ibm.com/run/primitives-get-started); the other is `Estimator`, which estimates the value of observable." + "Quantum computers can produce random results, so you'll often want to collect a sample of the outputs by running the circuit many times. You can use the `Sampler` class to get measured data from a quantum Computer. `Sampler` is one of our two [primitives](https://docs.quantum.ibm.com/run/primitives-get-started); the other is `Estimator`, which estimates the value of observable. We will be using `Estimator` in this example." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "a72b8e00-89e1-492c-860c-775f2e72d68d", "metadata": { "tags": [] }, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import Sampler, Options\n", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/bp/ynvhmy5n0r35yx8vh_lgfnwm0000gp/T/ipykernel_8318/1131823703.py:7: DeprecationWarning: The Sampler and Estimator V1 primitives have been deprecated as of qiskit-ibm-runtime 0.23.0 and will be removed no sooner than 3 months after the release date. Please use the V2 Primitives. See the `V2 migration guide `_. for more details\n", + " estimator = Estimator(backend)\n" + ] + } + ], + "source": [ + "from qiskit_ibm_runtime import Estimator\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", "\n", - "options = Options()\n", - "options.resilience_level = 1\n", - "options.optimization_level = 3\n", "\n", "# Create an Estimator object\n", - "sampler = Sampler(backend_sim, options=options)\n", + "estimator = Estimator(backend)\n", + "\n", + "# Create observable\n", + "n_qubits = 2\n", + "observable = SparsePauliOp(\"Z\" * n_qubits)\n", + "\n", + "# The circuit and observable need to be transformed to only use supported instructions (instruction set architecture(ISA)).\n", + "pm = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", + "isa_circuit = pm.run(circ)\n", + "isa_observable = observable.apply_layout(isa_circuit.layout)\n", "\n", - "# Submit the circuit to Estimator\n", - "job = sampler.run(circ, shots=10000)" + "# Submit the circuit to Sampler\n", + "job = estimator.run(isa_circuit, isa_observable)" ] }, { @@ -172,29 +246,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "7e873269-7312-4f7c-a4af-cf87c8def5eb", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>> Job ID: csczppax35wg00811z00\n", + ">>> Job Status: JobStatus.QUEUED\n" + ] + } + ], "source": [ "jobid = job.job_id()\n", "print(f\">>> Job ID: {job.job_id()}\")\n", "print(f\">>> Job Status: {job.status()}\")" ] }, - { - "cell_type": "markdown", - "id": "db17081f-a469-4a38-bdec-5500e968a642", - "metadata": {}, - "source": [ - "
\n", - "Note: \n", - " Jobs submitted by using the qiskit_ibm_runtime provider are not yet supported by the qBraid Lab quantum jobs sidebar\n", - "
" - ] - }, { "cell_type": "markdown", "id": "23e66477-79dc-4e24-a964-07a486203348", @@ -209,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "7c477078-67be-46d8-8378-962f6a9b9dea", "metadata": { "tags": [] @@ -227,21 +299,27 @@ "source": [ "Now we will plot the results. \n", "\n", - "As sampler returns quasi probability of measurement, let's use `plot_distribution` with a binary expression. See [SamplerResult document](https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.SamplerResult) for more information." + "As estimator returns expected value of measurement, we just print out what it is." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "d0590e58-9eea-4589-a774-6652c37d2e9f", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.3944620971402633\n" + ] + } + ], "source": [ - "from qiskit.visualization import plot_distribution\n", - "\n", - "plot_distribution(result.quasi_dists[0].binary_probabilities())" + "print(job.result().values[0])" ] }, { @@ -249,7 +327,7 @@ "id": "03ec980a-f294-4498-82cf-cb4c35a1f3b6", "metadata": {}, "source": [ - "## 2. Using qBraid SDK" + "## 2. Using qBraid-SDK" ] }, { @@ -257,19 +335,30 @@ "id": "de67207f-3dd5-499e-bb72-0ad0100b3228", "metadata": {}, "source": [ - "By following [this lab demo](https://github.com/qBraid/qbraid-lab-demo/blob/045c7a8fbdcae66a7e64533dd9fe0e981dc02cf4/qbraid_sdk/ibm_batch_jobs_grovers.ipynb#L4) provided by qBraid, you can also use qbraid sdk to submit your ibm job and check job status. The following show how to do that.\n", + "You can also use qBraid-SDK to submit your IBM job and check job status. The following show how to do that.\n", "\n", - "First, check the qbraid version." + "First, check the qBraid version." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "5854118c-2d37-497e-934f-784bdc431b21", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'0.7.0.dev20240516020308'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import qbraid\n", "\n", @@ -286,22 +375,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "1029c085-6e9f-4f56-b525-8d0b294d6dd4", "metadata": { "tags": [] }, "outputs": [], "source": [ - "from qbraid import device_wrapper, job_wrapper, get_jobs\n", - "from qbraid.providers import QuantumJob\n", - "from qbraid.providers.exceptions import JobStateError\n", - "from qbraid.providers.ibm import QiskitBackend, QiskitJob, QiskitProvider\n", + "from qbraid.runtime.qiskit import QiskitRuntimeProvider\n", "import os\n", "\n", "os.environ[\"QISKIT_IBM_TOKEN\"] = \"\"\n", "ibmq_token = os.getenv(\"QISKIT_IBM_TOKEN\")\n", - "provider = QiskitProvider(qiskit_ibm_token=ibmq_token)" + "provider = QiskitRuntimeProvider(ibmq_token)" ] }, { @@ -314,12 +400,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "a79c8520-01f7-4146-860f-996b60cf914c", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "provider.get_devices()" ] @@ -329,12 +429,12 @@ "id": "a1fb6069-3a00-4250-ad6f-11851ef9089f", "metadata": {}, "source": [ - "`qbraid.providers.ibm` also supports useful filtering by `get_devices()`. You can quickly find the least busy backend by using `ibm_least_busy_gpu()`." + "You can quickly find the least busy device by using `ibm_least_busy_gpu()`, or use `get_devices()` to see all the available devices." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "6f562ceb-72d9-4be4-8e8b-15690286c558", "metadata": { "tags": [] @@ -342,7 +442,7 @@ "outputs": [], "source": [ "# ibm_device = provider.ibm_least_busy_qpu() # return least busy backend of provider\n", - "# ibm_device = provider.get_devices(operational=True, simulator=False) # return list of operational QPU backends\n", + "# ibm_device = provider.get_devices() # return list of all backends\n", "ibm_device = provider.get_device(\"ibm_kyoto\") # return backend by name" ] }, @@ -351,33 +451,12 @@ "id": "545c1b43-9679-4dd6-ad6c-3dc7a48833df", "metadata": {}, "source": [ - "To send the quantum circuit to the backend and check the job status in the right sidebar of qBraid Quantum lab, wrap the ibm backend with `device_wrapper`. If you insert the backend, which does not appear in the right sidebar panel's \"device\" section, this code will return an error." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "086710ef-5cac-4fe3-95f0-d071c8321cea", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# qbraid_ibm_device = QiskitBackend(ibm_device) # you can load device object directly as well\n", - "qbraid_ibm_device = device_wrapper(\"ibm_kyoto\")" - ] - }, - { - "cell_type": "markdown", - "id": "953be90a-d983-444d-8d1f-4ebe9cd9f0e8", - "metadata": {}, - "source": [ - "To send a quantum circuit, you can simply call `wrapped_device.run(circuit, options)`. See [API document](https://docs.qbraid.com/en/stable/sdk/devices.html#device-wrapper) for more information." + "To send the quantum circuit to the backend and check the job status in the right sidebar of qBraid Quantum lab, we just run the device." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "5c92dd6f-c82f-46a5-bf80-07bdfc2f254d", "metadata": { "tags": [] @@ -385,7 +464,7 @@ "outputs": [], "source": [ "# Must have IBM credential to submit jobs using premium backends\n", - "qbraid_ibm_job = qbraid_ibm_device.run(circ, shots=20000)" + "qbraid_ibm_job = ibm_device.run(circ, shots=200)" ] }, { @@ -393,63 +472,30 @@ "id": "52c2510a-2a69-4624-aede-56237b76fe53", "metadata": {}, "source": [ - "Shortly afterwards you should see your submitted job in the right side panel. If you cannot see your job, click the circulation icon at the top to refresh or select `All` for Provider.\n", - "\n", - "Also, you can check your job status by using `job_wrapper(job_id).` Please note that the `job_id` for `job_wrapper` must be a qBraidID, which you can get by adding `.id` to your job." + "Also, you can check your job status by using `status()`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "0bb09636-cfbf-480d-aa53-734cd5a11c07", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "job = job_wrapper(qbraid_ibm_job.id)\n", - "job.status()" - ] - }, - { - "cell_type": "markdown", - "id": "2c65961f-540e-43dc-b318-c57d86e31751", - "metadata": {}, - "source": [ - "You can use the `get_jobs` function to a return a list of your previously submitted quantum jobs, along with the status of each." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3523429e-3341-4a57-80e3-2ad2fb9d3871", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "get_jobs()" - ] - }, - { - "cell_type": "markdown", - "id": "4dedeb3d-a03c-4b70-b9f9-a7a7000eb045", - "metadata": {}, - "source": [ - "This `qBraidID` can be used to reinstantiate a qBraid QuantumJob object at any time, and even in a separate program, with no loss of information." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "848add3e-1059-4726-be73-855fb8a7adad", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "job = job_wrapper(\"\")\n", - "job.status()" + "qbraid_ibm_job.status()" ] }, { @@ -462,24 +508,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "3d39b177-e2a5-41d4-8752-a946cb475f7a", "metadata": { "tags": [] }, "outputs": [], "source": [ - "ibm_result = job.result()" + "ibm_result = qbraid_ibm_job.result()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "bc1bfa7a-b16e-4b71-b949-662f54a6c141", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from qbraid.visualization import plot_histogram, plot_distribution\n", "\n", @@ -496,10 +553,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "50af7aa6-bd20-4224-9a94-e447768d869d", - "metadata": {}, - "outputs": [], + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_distribution(ibm_result.measurement_counts())" ] @@ -507,7 +577,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "notebooks", "language": "python", "name": "python3" }, @@ -521,7 +591,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/qbraid_sdk/qbraid_sdk_providers.ipynb b/qbraid_sdk/qbraid_sdk_providers.ipynb index 6273ba2..1afe2ee 100644 --- a/qbraid_sdk/qbraid_sdk_providers.ipynb +++ b/qbraid_sdk/qbraid_sdk_providers.ipynb @@ -5,438 +5,166 @@ "id": "c7b38b09-44f5-46b5-9809-23de47ada6c5", "metadata": {}, "source": [ - "# QbraidProvider" + "# qBraid Runtime" ] }, { - "cell_type": "code", - "execution_count": 1, - "id": "23a8fa7a-872b-43eb-92a5-8b960b6215c3", - "metadata": {}, - "outputs": [], - "source": [ - "import qbraid\n", - "\n", - "from qbraid.providers import QbraidProvider" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "025cdd59-667f-476a-bea0-3499ab7c3610", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.5.0.dev'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qbraid.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b5e61fc3-5979-4eb9-8076-44a50cbcc8af", + "cell_type": "markdown", + "id": "2626ba66", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Device('name': Aquila, 'arn': arn:aws:braket:us-east-1::device/qpu/quera/Aquila),\n", - " Device('name': Aria 1, 'arn': arn:aws:braket:us-east-1::device/qpu/ionq/Aria-1),\n", - " Device('name': Aria 2, 'arn': arn:aws:braket:us-east-1::device/qpu/ionq/Aria-2),\n", - " Device('name': Aspen-M-3, 'arn': arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-3),\n", - " Device('name': Forte 1, 'arn': arn:aws:braket:us-east-1::device/qpu/ionq/Forte-1),\n", - " Device('name': Harmony, 'arn': arn:aws:braket:us-east-1::device/qpu/ionq/Harmony),\n", - " Device('name': Lucy, 'arn': arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy),\n", - " Device('name': SV1, 'arn': arn:aws:braket:::device/quantum-simulator/amazon/sv1),\n", - " Device('name': TN1, 'arn': arn:aws:braket:::device/quantum-simulator/amazon/tn1),\n", - " Device('name': dm1, 'arn': arn:aws:braket:::device/quantum-simulator/amazon/dm1),\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "provider = QbraidProvider()\n", - "\n", - "provider.get_devices()" + "This notebook will go through the four different runtime provider modules in the qBraid-SDK and go through the very basic functions and features of each one." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "6b2d696e-101f-4841-a174-ad23be105349", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "braket.aws.aws_device.AwsDevice" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "23a8fa7a-872b-43eb-92a5-8b960b6215c3", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "aws_device = provider.get_device(\"arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy\")\n", + "import qbraid\n", "\n", - "type(aws_device)" + "from qbraid.runtime.native import QbraidProvider\n", + "from qbraid.runtime.braket import BraketProvider\n", + "from qbraid.runtime.qiskit import QiskitRuntimeProvider\n", + "from qbraid.runtime.ionq import IonQProvider" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "223a2a27-d56b-4299-9983-d974b48f70b1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "qiskit_ibm_provider.ibm_backend.IBMBackend" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "aeebd839-8c32-4f8d-a1f9-2ea44a94668c", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "ibm_device = provider.get_device(\"ibm_brisbane\")\n", - "\n", - "type(ibm_device)" + "qbraid.__version__" ] }, { "cell_type": "markdown", - "id": "9d781221-9c07-42eb-bcad-63a666221356", + "id": "852ccc34-8193-49f4-a3c5-59bbdabf296f", "metadata": {}, "source": [ - "## BraketProvider" + "## QbraidProvider" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "b464cc6a-236b-4dae-b913-4dd263926a61", - "metadata": {}, + "execution_count": null, + "id": "1c5a92d6-c400-4c84-b558-23eb3a724785", + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "from qbraid.providers.aws import BraketProvider, BraketDevice, get_quantum_task_cost\n", - "\n", - "braket_provider = BraketProvider()" + "qbraid_provider = QbraidProvider()" ] }, { "cell_type": "markdown", - "id": "4f2f6b75-eb98-4d06-af70-bece8104dfa4", - "metadata": {}, - "source": [ - "### Query AWS devices" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ca0e47ed-f256-4aae-baea-fb56860821de", + "id": "a5b7eb31-779d-4112-9fa0-14ad6823e98f", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Device('name': Aquila, 'arn': arn:aws:braket:us-east-1::device/qpu/quera/Aquila),\n", - " Device('name': Forte 1, 'arn': arn:aws:braket:us-east-1::device/qpu/ionq/Forte-1),\n", - " Device('name': Lucy, 'arn': arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy)]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "braket_provider.get_devices(statuses=\"ONLINE\", types=[\"QPU\"])" + "### Query Qbraid Devices" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "48dbb5c6-79c5-4a53-ae00-aaba6546fd7d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('AWS', 'Oxford', 'Lucy')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "braket_device = BraketDevice(aws_device)\n", - "\n", - "braket_device.vendor, braket_device.provider, braket_device.name" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "87f2a8bc-c39a-447c-b81f-6d07a519e5cd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "8" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "a6b8f229-7f06-43d0-977b-7ad290f9341a", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "braket_device.queue_depth()" + "qbraid_provider.get_devices()" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "b6742d3e-ed2b-4e46-9133-2b0a89d68595", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-11T10:00:00Z\n" - ] - } - ], + "execution_count": null, + "id": "350ef769-c2d9-4ff3-90ed-6c957c64d658", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "_, _, next_availability_switch_utc = braket_device.availability_window()\n", + "qbraid_device = qbraid_provider.get_device(\"qbraid_qir_simulator\")\n", "\n", - "print(next_availability_switch_utc)" + "qbraid_device.metadata()" ] }, { "cell_type": "markdown", - "id": "fe137aab-50f5-42f1-9482-4f4e075b4e14", - "metadata": {}, - "source": [ - "### Query AWS quantum tasks" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f596e845-6b15-4ac5-8be1-87d32721a289", + "id": "9d781221-9c07-42eb-bcad-63a666221356", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "tagged_tasks = braket_provider.get_tasks_by_tag(\"test\")\n", - "\n", - "len(tagged_tasks)" + "## BraketProvider" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "6df4b123-1307-46f4-a1ff-ab0edcfc1f05", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Decimal('0.0037500000')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "b464cc6a-236b-4dae-b913-4dd263926a61", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "task_arn = tagged_tasks[0]\n", - "\n", - "get_quantum_task_cost(task_arn)" + "braket_provider = BraketProvider()" ] }, { - "cell_type": "code", - "execution_count": 13, - "id": "cf5240f8-cd00-44c6-ba2a-ed09e511f39f", + "cell_type": "markdown", + "id": "4f2f6b75-eb98-4d06-af70-bece8104dfa4", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Quantum Jobs

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
qBraid IDSubmittedStatus
aws_dm_sim-markovshama-qjob-i0b6sv9fzrz7xctm7a0j2024-01-10T21:05:42.927ZINITIALIZING
aws_dm_sim-markovshama-qjob-32djqimt1k9vbbd1klhb2024-01-10T00:47:03.073ZCOMPLETED
aws_dm_sim-markovshama-qjob-e9s3e9jxte9hwb18yu3g2024-01-08T23:20:11.933ZCOMPLETED
aws_dm_sim-markovshama-qjob-e5vxqgdjsx23tuzn1ryw2024-01-08T22:52:03.040ZCOMPLETED
aws_dm_sim-markovshama-qjob-dlln5g8q8vv5u421xsy52024-01-07T01:16:38.432ZCOMPLETED
Displaying 5/5 jobs matching query
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "qbraid.get_jobs(filters={\"tags\": {\"test\": \"123\"}})" + "### Query AWS devices" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "b5205ac8-97c7-436a-aebf-9b5e22ba6d24", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Quantum Jobs

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qBraid IDSubmittedStatus
aws_dm_sim-markovshama-qjob-e5vxqgdjsx23tuzn1ryw2024-01-08T22:52:03.040ZCOMPLETED
Displaying 1/1 jobs matching query
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "ca0e47ed-f256-4aae-baea-fb56860821de", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "qbraid.get_jobs(filters={\"vendorJobId\": task_arn})" + "braket_provider.get_devices()" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "a4f7f820-3167-43fe-b4a2-38e782111033", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "qbraid.providers.aws.job.BraketQuantumTask" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "48dbb5c6-79c5-4a53-ae00-aaba6546fd7d", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "qbraid_job = qbraid.job_wrapper(\"aws_dm_sim-markovshama-qjob-e5vxqgdjsx23tuzn1ryw\")\n", + "braket_device = braket_provider.get_device(\n", + " \"arn:aws:braket:::device/quantum-simulator/amazon/sv1\"\n", + ")\n", "\n", - "type(qbraid_job)" + "braket_device.metadata()" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "60cc8dc9-df83-4226-b488-f1d14d0ee4f4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qbraid_job.status()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "b53515be-54bc-46b2-a6fc-e592a417f03c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.00375" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "a438053a-3a8a-4961-937d-4717435afe63", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "qbraid_job.get_cost()" + "braket_device.availability_window()" ] }, { @@ -444,63 +172,37 @@ "id": "648d4dea-9a60-43e0-9e67-4d7144936061", "metadata": {}, "source": [ - "## QiskitProvider" + "## QiskitRuntimeProvider" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "8c288074-ff7f-4048-920d-61ff1c0d611f", "metadata": {}, "outputs": [], "source": [ - "from qbraid.providers.ibm import QiskitProvider, QiskitBackend\n", - "\n", - "provider = QiskitProvider()" + "qiskit_provider = QiskitRuntimeProvider(\"\")" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "45f284e9-6193-4fbc-b4e7-98259773fd40", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "provider.get_devices(operational=True, simulator=False)" + "qiskit_provider.get_devices()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "d6e8c6c5-85c8-492d-86ca-2a7ad91074c4", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'ibm_q_kyoto'" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "provider.ibm_least_busy_qpu()" + "qiskit_provider.ibm_least_busy_qpu()" ] }, { @@ -513,175 +215,74 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "39581b36-403f-43f8-a9ab-f9f2804acee4", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('IBM', 'ibm_brisbane')" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "qiskit_backend = QiskitBackend(ibm_device)\n", + "ibm_device = qiskit_provider.get_device(\"ibm_kyoto\")\n", "\n", - "qiskit_backend.provider, qiskit_backend.name" + "ibm_device.metadata()" ] }, { - "cell_type": "code", - "execution_count": 22, - "id": "db53d0d7-365b-4ba6-91b4-b520d361363d", + "cell_type": "markdown", + "id": "c37ca6a9-8976-4b35-96fc-1c63d8668466", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "qiskit_backend.status()" + "## IonQProvider" ] }, { "cell_type": "code", - "execution_count": 23, - "id": "c8189836-57b3-4304-8516-aa92a680e8cd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "65" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qiskit_backend.queue_depth()" - ] - }, - { - "cell_type": "markdown", - "id": "41bb203d-fb7e-4f74-98f4-a443abe94772", - "metadata": {}, + "execution_count": null, + "id": "1683b86c-41d1-440b-b8a0-5cf5bd81db2c", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "### Query IBM jobs" + "ionq_provider = IonQProvider(\"\")" ] }, { "cell_type": "code", - "execution_count": 24, - "id": "bcaf275a-2ded-45ed-8998-9a7238dfe72a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Quantum Jobs

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qBraid IDSubmittedStatus
ibm_q_qasm_simulator-markovshama-qjob-zy12imhp7n43what3zeh2024-01-10T21:05:50.850ZCOMPLETED
ibm_q_qasm_simulator-markovshama-qjob-l26uo6t1lb4gmnlupy5d2024-01-10T00:47:15.785ZCOMPLETED
ibm_q_qasm_simulator-markovshama-qjob-z1jx9zhp2eik8ldub6dy2024-01-08T23:20:21.427ZINITIALIZING
ibm_q_qasm_simulator-markovshama-qjob-5tmp37mfo7cqqtqomer32024-01-08T22:52:12.400ZINITIALIZING
Displaying 4/4 jobs matching query
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "2dab848e-b75e-45ac-b3ff-dd7e00a91afa", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "qbraid.get_jobs(\n", - " filters={\n", - " \"tags\": {\"test\": \"*\"},\n", - " \"qbraidDeviceId\": QiskitProvider.ibm_to_qbraid_id(\"ibmq_qasm_simulator\"),\n", - " }\n", - ")" + "ionq_provider.get_devices()" ] }, { - "cell_type": "code", - "execution_count": 25, - "id": "8e4d47a7-2ce8-47bf-b5f8-ad93df9614ac", + "cell_type": "markdown", + "id": "01fa7a1d-ad5c-48f9-a950-bdad488ff94e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "qbraid.providers.ibm.job.QiskitJob" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "qbraid_job = qbraid.job_wrapper(\n", - " \"ibm_q_qasm_simulator-markovshama-qjob-zy12imhp7n43what3zeh\"\n", - ")\n", - "\n", - "type(qbraid_job)" + "### Query IonQ devices" ] }, { "cell_type": "code", - "execution_count": 26, - "id": "5968f678-4688-420f-b103-05adbedb0106", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "0de949ae-88fb-4349-b8eb-4fd1ef925b4a", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "qbraid_job.status()" + "ionq_device = ionq_provider.get_device(\"qpu.aria-1\")\n", + "\n", + "ionq_device.metadata()" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "notebooks", "language": "python", "name": "python3" }, @@ -695,7 +296,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/qbraid_sdk/qbraid_sdk_transpiler.ipynb b/qbraid_sdk/qbraid_sdk_transpiler.ipynb index 1c8edb2..af80feb 100644 --- a/qbraid_sdk/qbraid_sdk_transpiler.ipynb +++ b/qbraid_sdk/qbraid_sdk_transpiler.ipynb @@ -13,38 +13,26 @@ "execution_count": 1, "id": "331a496f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjain/anaconda3/envs/notebooks/lib/python3.11/site-packages/pydantic/_migration.py:290: UserWarning: `pydantic.utils:deep_update` has been removed. We are importing from `pydantic.v1.utils:deep_update` instead.See the migration guide for more details: https://docs.pydantic.dev/latest/migration/\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import numpy as np\n", "\n", - "from qbraid import __version__ as qbraid_version\n", - "from qbraid import circuit_wrapper, SUPPORTED_QPROGRAMS\n", + "from qbraid.programs import QPROGRAM_REGISTRY\n", "from qbraid.interface import (\n", " circuits_allclose,\n", " assert_allclose_up_to_global_phase,\n", " random_circuit,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "25eb5d25-ad67-41cd-bfe0-c67a6fa88608", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.5.0.dev'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qbraid_version" + ")\n", + "from qbraid.transpiler import transpile" ] }, { @@ -57,31 +45,31 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "0c41ae38", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'cirq': 'cirq.circuits.circuit.Circuit',\n", - " 'qiskit': 'qiskit.circuit.quantumcircuit.QuantumCircuit',\n", - " 'pennylane': 'pennylane.tape.tape.QuantumTape',\n", - " 'pyquil': 'pyquil.quil.Program',\n", - " 'pytket': 'pytket._tket.circuit.Circuit',\n", - " 'braket': 'braket.circuits.circuit.Circuit',\n", - " 'openqasm3': 'openqasm3.ast.Program',\n", - " 'qasm2': 'str',\n", - " 'qasm3': 'str'}" + "{'cirq': cirq.circuits.circuit.Circuit,\n", + " 'qiskit': qiskit.circuit.quantumcircuit.QuantumCircuit,\n", + " 'pennylane': pennylane.tape.tape.QuantumTape,\n", + " 'pyquil': pyquil.quil.Program,\n", + " 'pytket': pytket._tket.circuit.Circuit,\n", + " 'braket': braket.circuits.circuit.Circuit,\n", + " 'openqasm3': openqasm3.ast.Program,\n", + " 'qasm2': str,\n", + " 'qasm3': str}" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "SUPPORTED_QPROGRAMS" + "QPROGRAM_REGISTRY" ] }, { @@ -94,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "da3079f7", "metadata": {}, "outputs": [], @@ -137,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "8710a671", "metadata": {}, "outputs": [ @@ -166,7 +154,7 @@ " └───┘└───┘└─────┘ └────────┘ └────────┘ " ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -176,63 +164,41 @@ "qiskit_circuit.draw()" ] }, - { - "cell_type": "markdown", - "id": "970c8b71", - "metadata": {}, - "source": [ - "Applying the circuit wrapper and transpiling to braket and cirq" - ] - }, { "cell_type": "code", - "execution_count": 6, - "id": "fd0d8197", - "metadata": {}, - "outputs": [], - "source": [ - "wrapped_circuit = circuit_wrapper(qiskit_circuit)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "7f1ef479", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "This program uses OpenQASM language features that may not be supported on QPUs or on-demand simulators.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "T : |0|1|2 | 3 |4|5|6|7| 8 | 9 | 10 |\n", - " \n", - "q0 : -H-X-S--Rx(0.79)----V-------SWAP------C-----------\n", - " | | \n", - "q1 : -H-X-Si-Ry(1.57)----S-H-S---|----SWAP-X-----------\n", - " | | \n", - "q2 : -H-Y-T--Rz(2.36)----S-H-C-X-SWAP-|----C-----------\n", - " | | | | \n", - "q3 : -H-Z-Ti-PHASE(0.39)-S---X-C-H----SWAP-PHASE(0.79)-\n", - "\n", - "T : |0|1|2 | 3 |4|5|6|7| 8 | 9 | 10 |\n" + "T : │ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │\n", + " ┌───┐ ┌───┐ ┌───┐ ┌──────────┐ ┌───┐ \n", + "q0 : ─┤ H ├─┤ X ├─┤ S ├───┤ Rx(0.79) ├─────┤ V ├──────x─────────────────────●────────\n", + " └───┘ └───┘ └───┘ └──────────┘ └───┘ │ │ \n", + " ┌───┐ ┌───┐ ┌────┐ ┌──────────┐ ┌────┐ │ ┌─┴─┐ \n", + "q1 : ─┤ H ├─┤ X ├─┤ Si ├──┤ Ry(1.57) ├────┤ Vi ├──────┼────────x──────────┤ X ├──────\n", + " └───┘ └───┘ └────┘ └──────────┘ └────┘ │ │ └───┘ \n", + " ┌───┐ ┌───┐ ┌───┐ ┌──────────┐ ┌───────┐ │ │ \n", + "q2 : ─┤ H ├─┤ Y ├─┤ T ├───┤ Rz(2.36) ├───┤ ISWAP ├────x────────┼────────────●────────\n", + " └───┘ └───┘ └───┘ └──────────┘ └───┬───┘ │ │ \n", + " ┌───┐ ┌───┐ ┌────┐ ┌─────────────┐ ┌───┴───┐ │ ┌──────┴──────┐ \n", + "q3 : ─┤ H ├─┤ Z ├─┤ Ti ├─┤ PHASE(0.39) ├─┤ ISWAP ├─────────────x─────┤ PHASE(0.79) ├─\n", + " └───┘ └───┘ └────┘ └─────────────┘ └───────┘ └─────────────┘ \n", + "T : │ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │\n" ] } ], "source": [ - "braket_circuit = wrapped_circuit.transpile(\"braket\")\n", + "braket_circuit = transpile(qiskit_circuit, \"braket\")\n", "print(braket_circuit)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "2a6eb7e7", "metadata": {}, "outputs": [ @@ -240,18 +206,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "q_0: ───H───X───S──────Rx(0.25π)───X^0.5────────────────×───────@────────\n", - " │ │\n", - "q_1: ───H───X───S^-1───Ry(0.5π)────X^-0.5───────────────┼───×───X────────\n", - " │ │\n", - "q_2: ───H───Y───T──────Rz(0.75π)───S────────H───@───X───×───┼───@────────\n", - " │ │ │ │\n", - "q_3: ───H───Z───T^-1───Z^(1/8)─────S────────────X───@───H───×───@^0.25───\n" + " ┌──┐\n", + "0: ───H───X───S──────Rx(0.25π)───X^0.5─────×─────@────────\n", + " │ │\n", + "1: ───H───X───S^-1───Ry(0.5π)────X^-0.5────┼×────X────────\n", + " ││\n", + "2: ───H───Y───T──────Rz(0.75π)───iSwap─────×┼────@────────\n", + " │ │ │\n", + "3: ───H───Z───T^-1───Z^(1/8)─────iSwap──────×────@^0.25───\n", + " └──┘\n" ] } ], "source": [ - "cirq_circuit = wrapped_circuit.transpile(\"cirq\")\n", + "cirq_circuit = transpile(qiskit_circuit, \"cirq\")\n", "print(cirq_circuit)" ] }, @@ -267,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "665c6c0b", "metadata": {}, "outputs": [ @@ -277,7 +245,7 @@ "True" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -308,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "afe95c34", "metadata": {}, "outputs": [ @@ -316,32 +284,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "num_qubits: 10\n", - "depth: 9\n", - "op_density: 0.81\n", - "matrix dimension: (1024, 1024)\n", + "num_qubits: 8\n", + "depth: 10\n", + "op_density: 0.85\n", + "matrix dimension: (256, 256)\n", "\n", - " ┌───┐ ┌──┐ ┌──┐ ┌───┐ ┌──────┐ ┌──────┐ ┌──────┐\n", - "0: ────H───────S──────Z─────S─────X─────Y────S──────────H──────────×─────────\n", - " │ │\n", - "1: ───────────────────X─────X─────┼@─────────H──────────iSwap──────┼────T────\n", - " ││ │ │\n", - "2: ─────@─────────────X──────────S┼┼────T─────────X─────┼────@─────┼─────────\n", - " │ ││ │ │ │ │\n", - "3: ────T┼──────Z──────X─────@─────┼┼────Z────iSwap┼─────iSwap┼─────×─────────\n", - " │ │ │ ││ │ │ │\n", - "4: ─────┼×──────×─────┼─────X─────┼┼────H────┼────@──────────@─────iSwap─────\n", - " ││ │ │ ││ │ │\n", - "5: ────Z┼┼─────H┼─────┼Y────Y─────┼┼─────────iSwap──────S──────────┼─────────\n", - " ││ │ │ ││ │\n", - "6: ─────┼┼─────Z┼─────┼─────Z────T┼┼─────────Z─────────────────────┼────Z────\n", - " ││ │ │ ││ │\n", - "7: ─────┼×──────┼─────┼X─────────S┼┼────────────────────@──────────┼────@────\n", - " │ │ │ ││ │ │ │\n", - "8: ────Z┼───────×─────┼─────Z─────┼@────@────Z──────────┼──────────┼────X────\n", - " │ │ │ │ │ │\n", - "9: ─────@──────X──────@───────────@─────X───────────────@──────────iSwap─────\n", - " └───┘ └──┘ └──┘ └───┘ └──────┘ └──────┘ └──────┘\n" + " ┌───────┐ ┌───────┐ ┌──┐ ┌──────┐ ┌──────────┐ ┌──────┐ ┌──┐ ┌──┐ ┌──┐ ┌──────┐\n", + "0: ────────────────iSwap───────T───────────@─────iSwap──────────Z───────────X─────@──────@──────Z─────────\n", + " │ │ │ │ │ │\n", + "1: ─────iSwap──────┼─────@─────Y──────H────┼─────iSwap─────────────────────S┼─────┼×─────┼X─────T─────────\n", + " │ │ │ │ │ ││ │\n", + "2: ────Z┼──────────┼────T┼─────Y───────────┼─────@──────────────×───────────@─────┼×─────┼──────S─────────\n", + " │ │ │ │ │ │ │ │\n", + "3: ─────┼────@─────┼────Z┼─────@───────────┼─────┼────iSwap─────┼────X─────Y──────┼──────┼──────X─────────\n", + " │ │ │ │ │ │ │ │ │ │ │ │\n", + "4: ────@┼────┼─────iSwap─┼─────┼Z─────iSwap┼─────X────┼─────────×──────────@──────┼S─────┼X─────┼iSwap────\n", + " ││ │ │ │ │ │ │ │ │ ││ ││\n", + "5: ────@┼────┼───────────@─────┼──────iSwap┼──────────iSwap─────S──────────┼──────┼Y─────@┼─────┼┼────────\n", + " │ │ │ │ │ │ │ ││\n", + "6: ─────iSwap┼─────────────────@───────────┼─────×──────────────iSwap──────@──────┼X─────Y┼─────┼iSwap────\n", + " │ │ │ │ │ │ │\n", + "7: ──────────@─────H───────────Z───────────@─────×──────────────iSwap─────────────X───────@─────@─────────\n", + " └───────┘ └───────┘ └──┘ └──────┘ └──────────┘ └──────┘ └──┘ └──┘ └──┘ └──────┘\n" ] } ], @@ -374,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "id": "bbe4be5f", "metadata": {}, "outputs": [ @@ -387,13 +351,13 @@ } ], "source": [ - "braket_circuit = circuit_wrapper(circuit_start).transpile(\"braket\")\n", + "braket_circuit = transpile(circuit_start, \"braket\")\n", "print(type(braket_circuit))" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "d2813ee0", "metadata": {}, "outputs": [ @@ -406,13 +370,13 @@ } ], "source": [ - "pyquil_circuit = circuit_wrapper(braket_circuit).transpile(\"pyquil\")\n", + "pyquil_circuit = transpile(braket_circuit, \"pyquil\")\n", "print(type(pyquil_circuit))" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "fbb476fb", "metadata": {}, "outputs": [ @@ -425,16 +389,25 @@ } ], "source": [ - "qiskit_circuit = circuit_wrapper(pyquil_circuit).transpile(\"qiskit\")\n", + "qiskit_circuit = transpile(pyquil_circuit, \"qiskit\")\n", "print(type(qiskit_circuit))" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "fc405e6e", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjain/anaconda3/envs/notebooks/lib/python3.11/site-packages/qiskit_braket_provider/providers/adapter.py:441: UserWarning: The Qiskit circuit contains barrier instructions that are ignored.\n", + " warnings.warn(\n", + "WARNING - This program uses OpenQASM language features that may not be supported on QPUs or on-demand simulators.\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -444,13 +417,13 @@ } ], "source": [ - "pytket_circuit = circuit_wrapper(qiskit_circuit).transpile(\"pytket\")\n", + "pytket_circuit = transpile(qiskit_circuit, \"pytket\")\n", "print(type(pytket_circuit))" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "072f6aeb", "metadata": {}, "outputs": [ @@ -463,7 +436,7 @@ } ], "source": [ - "circuit_finish = circuit_wrapper(pytket_circuit).transpile(\"cirq\")\n", + "circuit_finish = transpile(pytket_circuit, \"cirq\")\n", "print(type(circuit_finish))" ] }, @@ -477,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "5f6f3633", "metadata": {}, "outputs": [ @@ -485,7 +458,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(1024, 1024)\n" + "(256, 256)\n" ] } ], @@ -496,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "id": "9f461918", "metadata": {}, "outputs": [ @@ -533,7 +506,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/qbraid_sdk/qbraid_sdk_transpiler_braket_qiskit.ipynb b/qbraid_sdk/qbraid_sdk_transpiler_braket_qiskit.ipynb index 5d93252..a1b4e6a 100644 --- a/qbraid_sdk/qbraid_sdk_transpiler_braket_qiskit.ipynb +++ b/qbraid_sdk/qbraid_sdk_transpiler_braket_qiskit.ipynb @@ -15,9 +15,18 @@ "execution_count": 2, "id": "06a5e7a7-9d41-4a2e-857d-1144e365467c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjain/anaconda3/envs/notebooks/lib/python3.11/site-packages/pydantic/_migration.py:290: UserWarning: `pydantic.utils:deep_update` has been removed. We are importing from `pydantic.v1.utils:deep_update` instead.See the migration guide for more details: https://docs.pydantic.dev/latest/migration/\n", + " warnings.warn(\n" + ] + } + ], "source": [ - "from qbraid.transpiler import Conversion, ConversionGraph, convert_to_package" + "from qbraid.transpiler import Conversion, ConversionGraph, transpile" ] }, { @@ -36,7 +45,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -122,26 +131,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:\n", - "Successfully transpiled using conversions: qiskit -> qasm3 -> braket\n" + "INFO - \n", + "Successfully transpiled using conversions: qiskit -> braket\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "T : |0|1|\n", - " \n", - "q0 : -H-C-\n", - " | \n", - "q1 : ---X-\n", - "\n", - "T : |0|1|\n" + "T : │ 0 │ 1 │\n", + " ┌───┐ \n", + "q0 : ─┤ H ├───●───\n", + " └───┘ │ \n", + " ┌─┴─┐ \n", + "q1 : ───────┤ X ├─\n", + " └───┘ \n", + "T : │ 0 │ 1 │\n" ] } ], "source": [ - "braket_circuit = convert_to_package(qiskit_circuit, \"braket\", conversion_graph=graph)\n", + "braket_circuit = transpile(qiskit_circuit, \"braket\", conversion_graph=graph)\n", "\n", "print(braket_circuit)" ] @@ -181,7 +191,7 @@ "metadata": {}, "outputs": [], "source": [ - "graph.add_conversion(conversion)" + "graph.add_conversion(conversion, overwrite=True)" ] }, { @@ -192,7 +202,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -215,7 +225,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:\n", + "INFO - \n", "Successfully transpiled using conversions: qiskit -> braket\n" ] }, @@ -223,18 +233,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "T : |0|1|\n", - " \n", - "q0 : -H-C-\n", - " | \n", - "q1 : ---X-\n", - "\n", - "T : |0|1|\n" + "T : │ 0 │ 1 │\n", + " ┌───┐ \n", + "q0 : ─┤ H ├───●───\n", + " └───┘ │ \n", + " ┌─┴─┐ \n", + "q1 : ───────┤ X ├─\n", + " └───┘ \n", + "T : │ 0 │ 1 │\n" ] } ], "source": [ - "braket_circuit = convert_to_package(qiskit_circuit, \"braket\", conversion_graph=graph)\n", + "braket_circuit = transpile(qiskit_circuit, \"braket\", conversion_graph=graph)\n", "\n", "print(braket_circuit)" ] @@ -256,7 +267,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/qbraid_sdk/qbraid_sdk_transpiler_cirq_stim.ipynb b/qbraid_sdk/qbraid_sdk_transpiler_cirq_stim.ipynb index bdff350..de90103 100644 --- a/qbraid_sdk/qbraid_sdk_transpiler_cirq_stim.ipynb +++ b/qbraid_sdk/qbraid_sdk_transpiler_cirq_stim.ipynb @@ -7,7 +7,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install 'qbraid==0.5.0.dev20240110205451' --quiet\n", + "!pip install qbraid==0.7.0.dev20240516020308 --quiet\n", "!pip install qiskit --quiet\n", "!pip install stimcirq --quiet\n", "!pip install matplotlib --quiet" @@ -109,7 +109,7 @@ "metadata": {}, "outputs": [], "source": [ - "from qbraid import SUPPORTED_QPROGRAMS\n", + "from qbraid.programs import QPROGRAM_REGISTRY\n", "from qbraid.transpiler import ConversionGraph" ] }, @@ -122,11 +122,15 @@ { "data": { "text/plain": [ - "{'cirq': 'cirq.circuits.circuit.Circuit',\n", - " 'qiskit': 'qiskit.circuit.quantumcircuit.QuantumCircuit',\n", - " 'openqasm3': 'openqasm3.ast.Program',\n", - " 'qasm2': 'str',\n", - " 'qasm3': 'str'}" + "{'cirq': cirq.circuits.circuit.Circuit,\n", + " 'qiskit': qiskit.circuit.quantumcircuit.QuantumCircuit,\n", + " 'pennylane': pennylane.tape.tape.QuantumTape,\n", + " 'pyquil': pyquil.quil.Program,\n", + " 'pytket': pytket._tket.circuit.Circuit,\n", + " 'braket': braket.circuits.circuit.Circuit,\n", + " 'openqasm3': openqasm3.ast.Program,\n", + " 'qasm2': str,\n", + " 'qasm3': str}" ] }, "execution_count": 6, @@ -135,7 +139,7 @@ } ], "source": [ - "SUPPORTED_QPROGRAMS" + "QPROGRAM_REGISTRY" ] }, { @@ -156,7 +160,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -217,7 +221,7 @@ "outputs": [ { "data": { - "image/png": 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", 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Hh8uXLzNhwoRCP03cioYNG3LgwAEuXryY58OW69ChQwDUqlULoMBGlLkN/a79vSjXpdFobutaVCoViqLcNK6bna+g5fkdOz9dunQhNDT0httc+/RU0orjPVfcr1X9+vUBW/uczp07F/ZSCq0wcY0cOZLnnnuO5cuX88QTT7Bs2TICAgLo27evfRur1Up4eDiLFy/O93jXJ0j5/V31ej3btm1j8+bNrF69mnXr1rF06VK6d+/O77//ftvv+1y38rfI/TscOXLEYbmHhwc9e/YEKLAxcn7XevbsWXr06EH9+vX54IMPqFq1KjqdjjVr1vDhhx/e1nfZ7X4uhSQDRRIWFoZer+f06dN51p08ebLQx8nJyQHyZtz5OXz4MKdOneK7775zaMi3YcOGm+5bvXp1rFYrZ8+edXiKKGysAwcO5Pvvv2fBggW8+OKLedanpaXxyy+/0KJFC3sykJuRp6SkOGx7fXHd7VxXQW7UmyMoKCjf4ml3KUasXr06hw4dwmq1Oty8T5w4YV+f++/mzZvJyspyKB04c+ZMoc9VEn+bohg4cCBvvfUWixYtumkykHvd+b2nT5w4QWhoKD4+PkWOoWbNmrRp04alS5cydepUVq5cyZAhQ/D09LRvExkZyR9//EHHjh1vK4FTq9X06NGDHj168MEHH/Dmm2/ywgsvsHnzZvtN91oldc3Xq1evHnXq1OHnn3/mo48+uu1j/vrrrxiNRlatWuXwcHF9dUquM2fOoCiKw+c6d7Cp6xtIitsn1QRFoNFo6NOnDz///DMXL160Lz9+/Djr168v9HFyW+k3a9asUOcExwxXURT+97//3XTffv36AfDxxx87LP/oo48KFefw4cNp1KgRb7/9dp7hPq1WK5MnTyY5OZkXXnjBvjy3SmLbtm32ZRaLhS+++MJh/9u5roLk3vyuT0Ry4zpx4oRDd6ODBw/etNW2q+jfvz9Xr151qMfOyclhzpw5+Pr62ovw+/Tpg9ls5ssvv7RvZ7Va7V39CqMk/jZF0b59e/r27ctXX33Fzz//nGe9yWRi+vTpAFSqVInmzZvz3XffOfzdjxw5wu+//07//v1vOY6RI0eye/duvvnmGxISEhyqCMDWQt9isfDaa6/l2TcnJyff9+H18uuW2rx5c8DWXS4/JXnN15s1axYJCQlMnDgRs9mcZ31Rnrzze1+lpqby7bff5rv9lStX+Omnn+y/p6WlsWDBApo3b+7Qy0MUDykZKKJXXnmFdevW0blzZ6ZMmWL/Qm7UqJG9yPxaly9fZtGiRYDtS+zgwYPMmzeP0NBQhyqCgtSvX5/IyEimT5/O5cuX8ff3Z8WKFYWqC2vevDmjR4/m008/JTU1lQ4dOrBx48ZCPyVqtVpWrFhB9+7d6dSpk8MIhN9//z379u3j+eeft3fjA2jUqBHt2rXjueeeIykpieDgYJYsWWIvDSmO6yqIXq+nYcOGLF26lLp16xIcHEzjxo1p3LgxDzzwAB988AF9+vThwQcfJC4ujs8//5xGjRq5xXDADz/8MPPmzWPChAns3buXGjVq8OOPP7Jz504++ugje/3ukCFDaNOmDU8//TRnzpyhfv36rFq1yn7TKcxYGCXxtymqBQsW0Lt3b4YNG8bAgQPp0aMHPj4+nD59miVLlhATE2Mfa+Ddd9+lX79+tG/fngcffNDezS6/PvBFMWLECKZPn8706dMJDg7O85TetWtXJk2axFtvvcWBAwfo3bs3Wq2W06dPs3z5cv73v/85jEmQn1dffZVt27Zx1113Ub16deLi4vj000+pUqUKnTp1KnC/krrm640ZM4YjR47w1ltv8ffffzNq1Chq1qxJZmYmR44c4YcffsDPz89eIngjvXv3RqfTMXDgQCZNmkRGRgZffvkl4eHhxMTE5Nm+bt26PPjgg/zzzz9UqFCBb775htjY2AKTB3GbSrv7QlmwdetWpWXLlopOp1Nq1aqlfP7553m69SlK3q6FarVaCQ8PV0aPHq2cOXPGYdvx48crPj4++Z7v2LFjSs+ePRVfX18lNDRUmThxonLw4ME83Y3yi8FgMCiPPfaYEhISovj4+CgDBw5ULl26VKSR+uLj45Wnn35aqV27tqLT6ezX8/XXX+e7/dmzZ5WePXvau809//zzyoYNG/J0LSzsdRX02uR3vbt27bL/ba6/xkWLFim1atVSdDqd0rx5c2X9+vUFdi189913HY6b2zVy+fLlDssLOwJaQSMQXq9r165Ko0aN8l0XGxur3H///UpoaKii0+mUJk2aOLxOueLj45UxY8Yofn5+SkBAgDJhwgRl586dCuDQ1a843nMFHaOg6yhoVM78ZGVlKe+9957SunVrxdfXV9HpdEqdOnWUadOm5fn8/PHHH0rHjh0VvV6v+Pv7KwMHDlSOHTvmsE1Bf4OCusMqiqJ07NhRAZSHHnqowDi/+OILpWXLloper1f8/PyUJk2aKP/3f/+nXLly5abXvXHjRmXw4MFKRESEotPplIiICGX06NEOo5Pm17WwJK85P1u2bFHuvvtupVKlSopWq1X8/f2VVq1aKTNnzlRiYmIctr3R33jVqlVK06ZNFS8vL6VGjRrKO++8o3zzzTd5Ysk9xvr165WmTZsqnp6eSv369Qv9+cuvK7O4MZWiSAuL4jBr1ixeeeWVctFgJbdhV9WqVdmxY4dDFzzhmn7++WeGDh3Kjh076Nixo7PDEeKGatSoQePGjfntt9+cHUq5IW0GRJE1adKEX375hdOnTzNkyJByM2GMu7h+/AqLxcKcOXPw9/enRYsWTopKCOHKpM2AuCVdu3YlOzvb2WGIfEybNg2DwUD79u0xGo2sXLmSXbt28eabb5Zqt0UhhPuQZECIMqZ79+68//77/Pbbb2RnZ1O7dm3mzJnD1KlTnR2aEMJFSZsBIYQQopyTNgNCCCFEOSfJgBBCCFHOSTIghBBClHOSDAghhBDlnCQDQgghRDknyYAQQghRzkkyIIQQQpRzkgwIIYQQ5ZwkA0IIIUQ5J8MRCyGEEDdhtVoxmUxYLBb77LQqlQqNRoOnpycqlcrJEd4eSQaEEEKI6xiNRjIzMzEYDGRlZWE0GgvcVqVS4enpibe3N15eXvj6+qLT6Uox2tsncxMIIYQQgKIopKWlkZSURGZm5m0dy9fXl5CQEHx9fd2i1ECSASGEEOWa1WolISGBpKQkcnJyivXYWq2WkJAQQkJCXDopkGRACCFEuZWVlcWlS5cwm80leh5PT0+qVq2Kl5dXiZ7nVkkyIIQQotyxWq3ExsaSmJhYqucNDw8nLCzM5UoJJBkQQghRrphMJs6fP4/JZHLK+b28vKhRowYeHq7Thl+SASGEEOWG0WgkKiqq2NsGFJVWq6VmzZou0+tABh0SQghRLhiNRs6dO+f0RADAbDYTFRVV4m0VCkuSASGEEGVe7s3XYrE4OxQ7s9nM+fPnXSImSQaEEEKUaYqiEB0d7RIlAtczGo1cuXLF2WFIMiCEEKJsS0lJue1BhEpSamoqaWlpTo1BkgEhhBBllslkcokn75u5fPmyU6sLJBkQQghRZl25cgV36DRnsViIiYlx2vklGRBCCFEmZWVlkZGR4ewwCi0lJcVpYx9IMiCEEKJMSkpKcnYIReasmCUZEEIIUebk5OSQmprq7DCKLCkpCavVWurnlWRACCFEmZOSklIibQUuX75MkyZN+Pnnn4v92GCbM8EZPQskGRBCCFHmJCcn39b+q1evZuHChcUUTdHcbuy3wnVmSRBCCCGKgdVqxWg03tYx1qxZw5kzZxg3bpzD8oiICPbs2VOikwwZDAYURSnVmQ2lZEAIIUSZkp2dXWLHVqlUeHp6otFoSuwcVqu11OcskGRACCFEmWIwGG66TWZmJu+88w59+vShRYsWdO3alYkTJ3Ls2DHuv/9+tm3bxpUrV2jSpAlNmjShT58+QP5tBl544QXatGlDTEwMjz76KG3atKFHjx788MMPAJw6dYoHH3yQNm3a0Lt3b1avXl0s11CcpJpACCFEmVKYG+mrr77Khg0bGD16NJGRkaSkpLB//37OnTvHxIkTycjIIDY2lmeeeQYAb2/vGx7ParUyefJkWrZsyVNPPcXq1at588030ev1zJkzh7vuuosePXqwfPlyXnjhBZo1a0aVKlVueA0BAQFFu/DbIMmAEEKIMqUwRezbt29n+PDh9pv99RYvXkxaWhoDBw4s1DmNRiMDBgzgoYceAqB///706NGDl19+mdmzZ9O3b18A2rdvz6BBg1i1ahVTpky5rWsoTlJNIIQQokwpTD99Pz8/Dh8+TFxcXLGdd9iwYfb/+/v7U6NGDfR6vb2KAaBmzZr4+fkRHR19w2OV9hDKkgwIIYQoUwpzI33yySc5c+YMvXr1YvTo0Xz66adcunTpls/p6elJcHCwwzJfX18qVKiQp1eAn5/fTccSKO2BhyQZEEIIUaYUpkte3759Wbt2Lc899xxhYWHMnz+foUOHsn379ls6p1qd/+20oF4HN0tYCjpeSZFkQAghRJlS2BtpWFgYo0aN4uOPP2bt2rUEBATw5ZdfAoVLKEpSaZ9fkgEhhBBlik6nu+F6i8VCenq6w7KQkBDCw8Ptswbq9fo825Smm11DcZPeBEIIIcoUvV5/wyF9MzMz6dmzJ7169aJevXp4e3uze/dujhw5wvTp0wFo2LAh69atY/bs2TRu3Bhvb2+6detWSldgu4bSJMmAEEKIMuVmN1K9Xs+oUaPYtWsXGzduxGq1Uq1aNV588UVGjhwJwMiRIzlx4gQ///wzCxcuJCIiolSTAS8vr1I7F4BKKe3+C0IIIUQJslqtHDt2zNlh3DK1Wk2DBg1kbgIhhBDiVqnV6lJ/si5O3t7e0oBQCCGEuF1BQUHODuGWOSN2SQaEEEKUOYGBgU7vHngrNBoNfn5+pX5eSQaEEEKUORqNhsDAQGeHUWTBwcGlPuAQSDIghBCijLp+eGB34KyYJRkQQghRJun1eqcUud+qoKAgtFqtU84tyYAQQogyq3Llyk4pdi8qDw8PKlas6LTzu/4rJIQQQtwiDw8PIiIinB3GTVWpUqXASY1KgyQDQgghyrSAgACXri4ICgrC19fXqTFIMiCEEKJMU6lUVK5cudQn/ykMLy8vp1YP5JLhiIUQQpRZ8fEwfz7k5MD06WbOnj1LTk6Os8MCbDMT1qpVCw8P508TJMmAEEKIMsNohF274PffYc0aOHTItjwkBBISwGQyERUVhdlsdmqcnp6e1KxZ0yUSAZBZC4UQQpQRv/8OgwdDdjZ4eNhKA3K9+KLt39yn8QsXLpCdne2UOL29valevbpTGwxeT0oGhBBClAlHj0Lr1rZk4No7m6cnxMZCQMB/yxRFIT4+nri4uFKLT6VSUaFCBUJCQlxuqGRpQCiEEKJMaNQIfvrJcZmHB4wa5ZgIgO3GHB4eTu3atfH09Czx2PR6PbVr1yY0NNTlEgGQagIhhBBlhNEI333nWCqQkwOTJhW8j5eXF7Vr1yYpKYmEhIRib0vg6elJaGioy0+cJNUEQggh3F5aGgwdCjt3wqJFEBUF//d/UK8eHD8OhbkPK4pCZmYmiYmJpKen31Y8AQEBhISEoNfrXToJyCUlA0IIIdxaTAz06wfnz9saEXbpYisd8PaGhg0LlwiArerA19cXX19fzGYzmZmZZGdnk5WVRXZ2NlarNd/91Go1er3e/uPj4+MyvQQKS0oGhBBCuK2TJ6FPH1t1wLp10LhxyZxHURTMZjNWqxWr1YpKpUKlUqFWq9FqtW7x9H8jkgwIIYRwS7t3w4ABUKGCLRGoWtXZEbkv6U0ghBDC7fz2G3TvDg0awPbtkgjcLveq1BDCySxWhTSTmeRsM1lmC1YFrIqCWqVCrQJvrYYgLy3+Oi0atXsXGwrhqr76ytZDYPBgWLwY9HpnR+T+JBkQ4gYURSHRYOZSmoFEg4l0Uw659Wr53eqvXeen8yBEr6Oav55gvfvXKQrhbIoCr70GM2fClCnw8cfgQoP4uTVpMyBEPsxWK5fSDJxNziTdZEHFfzf6osjdz0/nQWSQN9X89XiopXZOiKKyWODRR2HePHj9dXj++cL3EhA3J8mAENewKgonEzM4lZSJpQQ+GhqVirrBPtQL8UUt32RCFIrBAKNH29oJfPkl3H+/syMqeyQZEOJfKdlm9sSkkGYq+elN/T09aF0xkAAvbYmfSwh3lpQEAwfCgQOwbBncdZezIyqbJBkQ5V5uacCJxAzg1qoDiiq3TKB+iK+UEghRgAsXoG9f29TDq1dDmzbOjqjskmRAlGs5Vit/Xk4mPsvktBjCvXW0qxwkbQmEuMbhw7ZEwNPTNoZA3brOjqhsk28fUW6ZLVa2X0pyaiIAEJdlYvulJMyW/Ic6FaK82bIFOnWyDSa0a5ckAqVBkgFRLuVYFXZGJ5GSXbwzlN2qlGwzuy4nYbFKQZ0o35Yvtw0v3KaNLSmoWNHZEZUPkgyIckdRFP6+kkxStrlU2gcUhgIkGsz8fSUZqbkT5dXHH8PIkXD33bY2Av7+zo6o/JBkQJQ7F1INXM00OjuMfMVkGrmYZnB2GEKUKqsVnn0WHn8cnn4aFi4Enc7ZUZUvMgKhKFeyzBYOxqU5O4wbOhCbRri3J3qtDK0myj6zGR580JYAfPghPPGEsyMqn6RkQJQbiqKw72oKVhcvhrcqCvuupkp1gSjz0tNtsw4uWQI//CCJgDNJyYAoN6LTs4lzcs+BwlCA2Cwj0enZVPWXGVhE2RQbaxtA6NQpW9fB7t2dHVH5JiUDolxQFIWTSRnODqNITrtZvEIU1pkz0KEDXLlim35YEgHnk2RAlAvJ2WbSjCU/zHBxSjHmkJzt+iUZQhTFP//YEgGt1jaGQLNmzo5IgCQDopw4l5KZ75TDrkwFnEvOcnYYQhSbdevgzjshMhJ27oQaNZwdkcglyYAo84wWK5fSskt8TIE5M57g3ha1i+14CnApzYBJRiYUZcB339kmHLrzTti4EUJCnB2RuJYkA6LMi880FlsiYDRksXTOexz5a1cxHTF/SbFXWTrnPc4eP0J8lmuOiSBEYSgKvPUWTJhg+/npJ/D2dnZU4nqSDIgyLznbXGxVBMZsA8vmfsDRv0s4GYiLZdncDzh//CjJLjJkshBFZbHAtGnw/PMwcyZ88QV4SB82lyR/FlHmJbvQsMNFpYAkA8ItZWfD2LG2koB58+Dhh50dkbgRKRkQZZqiKHz27lsMrx9B9LnTvPfEJMa2rMv4to34+o2XMBmzAXhp7DCeGtwz32NM69uJVx8cTVz0Je5v3wSAZXM/YHj9CIbXj2DpnPcKPH/U8SPc374xL48bjiEzE4DE2BjmPv8kD3RsysgmNXh8QDc2rvjBvs+Rv3bx7D39AJj7/JN0qRaKSqVi/vz5xfGSCFHiUlJskw2tXm1LBiQRcH1SMiDKtCyzxT7i4PtPPEJ45Src+9RznDq4jzULvyYzLZXH3vmYroOH89lLz3Dx1Amq1a1v3//M4QNcOX+O4ZOfwD84hIdnvc0Xs2bQtlc/2vbqD0D1eg3yPfeZwwd47aExRDZqyrOffounl56UhHieGzkAlUpFv3vvxz84hP3bNvHpC09jyMhgwPiJVImsw6jHnmHJx+/Sa8RYGrRqS9Mwf7p06VTyL5gQtyk6Gvr1s40hsHGjrRuhcH1SMiDKNJP1v5b4FapU5bnPvqPfvffz+Ow59B0znq2//Mj5k8do33cgOk8vtv66wmH/ratW4OXtTbte/fHy9qZ9n7sAqF63AV0HDafroOHUqNcwz3lP7PubV+4fSb3mrXju8+/w9LKNJPj9R29jtVh5b+Xv3DPlSfqMuo8Zn86nY//BLP3kfYzZBgJDw7ijs20UlrrNW9J10HCGjxpNrVq1SuplEqJYHD0K7dtDWhrs2CGJgDuRZECUadf2yus7ZoLDun5jHwBg39aN+Pj507pHb3as/tk+J4DFYmHX2lW06dEXryI0fz68eyevPTSGJu078cycr9DqPAFblcXu39fQ6s5eKCikJSfaf5p36kZWehpRxw7nfx0yT4FwcTt2QKdOEBwMf/4JDfIvMBMuSqoJRJmmXNN0sFINxyfrilVroFarib8cDUDXwfewc80qju35i0at23Fo13ZSEuLpMujuQp/PZDTy5iPjiGzUlKc/nIfmmqbTaUmJZKalsmHZIjYsW5Tv/qmJiflfh+QCwoX99BOMGQPt2sHPP0NAgLMjEkUlyYAo0zSqgjsVqq5b17xTNwJDw9i2agWNWrdj268rCAwLp2mHzoU+n1ano0WXHvyzaT37t2+m1Z297Ous/1ZZdBk0nG5D7sl3//yqHAA0UoYnXNRnn8HUqXD33bBgAXh6OjsicSvkK0aUaR7q/274MefPOayLuRiF1WolrHIVADQaDZ3uGsru31eTkZrC33+so1P/IWg0Gvs+qpuMWKBSqXj83U9o0q4T7z8xyWFwIv/gEPQ+vlgtFpp16JLvT0BIqP04Dtehko+qcC2KAi++CFOm2MYS+OEHSQTcmXzDiDLNR/tf4de67+c7rFu76BsAWnT5b8q0roOHk5GawucznyU7K5Oug4Y57KPT2xoCZqanFXhOrU7HM3O+IrJJM96aPJ7Th/YDtmSjXe/+7P59DRdPncizX2rSf1UEnt6282Slp6ECfHSaPNsL4SxmMzz0ELzxBsyeDR9+CGq5m7g1qSYQZZpGrcLz3zL22OhLvDV5PHd0vpOTB/aybdUKOg8YSo36jezb12rYhGp16vPnul+pElmHWo2aOhzP00tPldp12bl2FRE1auEbEEi1OvUduiPmbvf85wuYNf4eXp84ltcWrqBa3fqMffoFjvy1ixkj76LnPfdSJbIuGanJnDt2hMN/bue7v44BtvYMPv4BrF+ygOAAf4zVK9K2bVtq1qxZwq+YEDeWmQkjRsDvv8PChbaBhYT7k1xOlHl6re2p+ukPP0er82TR+2+yb+tG+t17P1PeeD/P9l2H2BoMdi2g4eCU194jJLwi3741iw+fnsKf63/LdztvXz9e+up7AsPCeOXBUcRciCIwNIx3lq/hzqEj+WvDGr5+/QVWL/yajNRkxj79gn1fD62WaW9/hEaj4X8vPsPo0aPZunXr7b4UQtyW+Hjo3h22bbMNKCSJQNmhUhRppyzKtsdnPM/H77zFt38exj/o5lOl/bbgK+a/NZPPNv5FWESVUojwxu6oEEDNQJnZRThXVJRtVMHUVFizBlq2dHZEojhJyYAo87w9Cl/frigKG3/8gYat27tEIgAQ6q1zdgiinNu/3zaYkKLArl2SCJRF0mZAlHmehUgGsrOy+GfTeo78tYuLp44z49NvSyGymwvRa/HTycdUOM8ff8DQobZBhH77DcLDnR2RKAnyLSMEtgGBPpr+KD7+AQyb9Bitu/dxdkgARAb6ODsEUY59/z1MmAA9e8KyZeDr6+yIREmRNgOiXLBYFdacjcVsdZ+3u06jon9kBdQ3GDhJiJLy/vswfTqMHw9ffglarbMjEiVJ2gyIckGjVlHLzZ6yawX6SCIgSp3VCk89ZUsEnn8evv1WEoHyQKoJRLlRN9iHC6lZZF87e5GL8vJQUyfYvZIX4f6MRlu1wNKl8Mkn8Oijzo5IlBYpGRDlhlajpmWlQGeHUSitKgailSHdRClKS4P+/W2TDi1fLolAeSMlA6JcqeDjSXV/PRfSDM4OpUA1A/SE+8gg76L0xMRAv35w/rxtZMEuXZwdkShtkgyIW2bMsZBsNJNpsmBVFCwKqFW2mQL1HhoCvbToPdR5Jt1xtqbh/sRmGjFarLhSc0IV4OmhpnG4v7NDEeXIyZO2wYRycmDHDmjc2NkRCWeQ3gSi0LLMFi6lGUgymEjKNmO8pu792tv9tW8orVpFsJeWIL2Oqn56/DxdI/9MNZrZejGRHBfpXaDCNsNi12oh+HtKay1ROv78EwYMgIoVYd06qFrV2REJZ5FkQNyQoijEZZk4l5xJTKbxlo+jwpYkhOp1RAZ5U8nXy+kt5ZMMJrZfSsKqKE4tIVABapWKLtWCCfKS0QZF6fjtN9uEQ61awS+/QFCQsyMSziTJgMiXoihcTs/mSEI6WWaL/WZ+u3KP46lRUz/El1qB3k6tRkjONrPjkq2EwBkfhNwSgc5VQwj0khIBUTq++gomTYLBg2HxYvh3Zm5RjkkyIPLIzrGwPzaVmIxbLwkorGAvLa0qBeLrxCF30005/HUlmTRjTqmfO8DTgzYRQTLksCgVigKvvQYzZ8KUKfDxx6Ap/NQdogyTZEDYKYpCdHo2B2JTS+1JWQWoVNA4zJ9IJ5YSWBWFU0kZHE/IAIqnFKQguVfYINSPusEysJAoHTk5tu6CX3wBr79uG1BI3noilyQDArAlAgfj0jiXkuW0GCr6eNI2IgiN2nnfUKnZZv65mkKaMafYqkZy5R4vwNODVpUCCZCGgqKUGAwwerStncCXX8L99zs7IuFqJBkQWBWFvTEpXErPdnYohOp1dKgShIcTB9yxKgoxGUbOpWQSn2W67aQgd/8wbx2RgT5U9PWU0gBRapKSYOBAOHDANphQ//7Ojki4IkkGyjlFUdh7NZWLLjQIT5i3jg6Vg51aQpAr3ZRDVEoWF1Kz7JMc3Sw5uHa9Vq2ieoA3tQK9ndouQpRPFy5A376QkACrV0ObNs6OSLgqSQbKuSPxaZxKynR2GHlE+HrRNiLQZQYsUhSFrBwLKdlmkv/9yTRbsFgVDNnZqFXgrdfjo9UQ5KW1/+g9NC5zDaJ8OXTINqqgp6dtDIG6dZ0dkXBlkgyUY/FZRrZfSnJ2GAVqWTGA6gHezg7jpr7++mtCQ0MZPHiws0MRAoAtW2zdBiMjYc0a26BCQtyIzIRSTuVYreyJSXF2GDd0IDYNg9ni7DBu6urVq5w+fdrZYQgBwLJltuGF27aFrVslERCFI8lAOXUkPh1DjmtP5WtVFPZdTcWVC68uX75MTk4OmZmZJCQkODscUc59/DGMGgX33GPrOeDn5+yIhLuQZKAcSjSYnNqFsLAUIDbL6BK9HAqydetW+/83b97sxEhEeWa1wrPPwuOPw9NPw4IFoJORrUURSDJQDp1KysCdmrSdTMxwydKB2NhYh+qBY8eOcfXqVSdGJMojkwnGj4fZs+HDD+Hdd8GJPXOFm5K3TDmTZbYQk2F0qal7bybdlENSttnZYeSxfft21Nd866rVajZu3OjEiER5k55uG0Ng2TJYsgSeeMLZEQl3JclAOXM+1fWrB66nAs4lu1b3x6SkJI4ePYrV+l+7C6vVypkzZ4iOjnZiZKK8iI2FO++0TUO8di2MHOnsiIQ7k1FQyhGronAuxfk31ZfHDQfg1YUrCrW9AkSnZ9Mkx4KXh2vMqpKcnGwfPyC3CkOn06HRaDAYXGcAJ1E2nTlj6zFgMMD27dCsmbMjEu5OkoFyJMlgwmRxpwqC/yhAbKbRZcYdiIyM5KWXXkKlUvG///0Pg8HAjBkznB2WKAf++QfuuguCg2HjRqhRw9kRibJAqgnKkWQXrHcvLBWuF39uyYBKpXLJBo6i7Fm7Frp1sw0mtHOnJAKi+EgyUI6kZJvdqhfBtRQgyeBayUAutVotyYAocd99Z2ss2KOHrUQgJMTZEYmyRJIBNzFr1ixUKhUnTpxgxIgR+Pv7ExISwuOPP052tmM//EWLFtGyZUv0ej3BwcGMGjWKS5cukZRttvcieHnccJ4YeCeXzpzi5fvuZnTzWkzs0oKfv5rrcKwjf+1ieP0Idq5dxY+f/4+JXVsyqmlNZk0YQcyFKPt2Sz5+lxGNq5GalJgn9s9eeoZxretjMuY/XoDZZOKHj2fzzLA+jGtVjzF3RPLivUM4vHunw3Znzp1DpVLx3nvv8cUXXxAZGYmnpyetW7fmn3/+yXPcEydOcPfddxMcHIyXlxetWrVi1apVhXm5i0RKBkRJUhR46y2YMAEeeABWrgRv16gtE2WIJANuZsSIEWRnZ/PWW2/Rv39/Pv74Yx5++GH7+jfeeIP77ruPOnXq8MEHH/DEE0+wceNGunTpQlyi4zwEmampvD5xDDXqN2T8szOpXKs2C997g33bNuU5709ffsLfG9Yy+IFHGPbwVE4d3Mv/nplqX9918N1YcnLYueYXh/3MJhO7f19Nu9790Xl65XtNhsx0Ni7/gUZtOjB2+guMmPo0qUmJvD5xDFHHj9i3+3fSQL7//nveffddJk2axOuvv8758+cZNmwYZvN/JQdHjx6lXbt2HD9+nBkzZvD+++/j4+PDkCFD+Omnnwr/gheCWjp1ixJiscC0afD88zBzJsybBx7S0kuUAHlbuZmaNWvyyy+2G+6jjz6Kv78/n376KdOnTycgIICZM2fy+uuv8/zzz9v3GTZsGHfccQfrvv+O4Y88Zl+eFHeVae98TLfBdwPQY/hoHunRho0//kCLLt0dzms2Gnnvpw1o/x3WzMc/gG/efJmLp05QrW59KlWvSb3mLdn260r6j33Avt++rRvJSE2h66C7C7wmH/9APtv4l/3YAL3uuZfH+ndhzaJvePSNDxy2v3jxIqdPnyYoKAiAevXqMXjwYNavX8+AAQMAePzxx6lWrRr//PMPnp6eAEyZMoVOnTrx7LPPMnTo0EK+4jcn1QSiJGRnw9ix8NNPtiTgmpxfiGInjzRu5tFHH3X4fdq0aQCsWbOGlStXYrVaGTFiBAkJCfafihUrElm7Nkf+dix29/L2oeug4fbftToddZo0Jzb6Qp7z3jlspMPNukGrtgAO23Ydcg+nD+7j6sXz9mXbfl1JaKUIGrVpX+A1aTQa+7GtVivpKclYLDlENmpG1LHDebYfOXKkPREA6Ny5MwDnzp0DbGMAbNq0iREjRpCenm5/HRITE+nTpw+nT5/m8uXLBcZTVFJNIIpbSoqt6+Dq1bZkQBIBUdKkZMDN1KlTx+H3yMhI1Go158+ftz+hXr9Nrur1HHO/kIqV7C3ic/n4B3Lh5PE8+4ZVquzwu69/IAAZqan2ZR37DeLbN2ey7deVjHj0KTLT09i75Q8GjJ+Y5zzX2/zTMlZ9O48rUWfIuaa4P7xKtTzbVqvmuCw3MUhOTgbgzJkzKIrCSy+9xEsvvZTv+eLi4qhcuXK+64pKo3GNsQ9E2RAdDf36wZUrtoaCHTo4OyJRHkgy4OauvclarVZUKhVr167Nc4MymC0cTXNsja8u4CaW31OuWn3zbX0DAmnZrSfb/00G/lz/G2aTkS7XlD7kZ+uqFXzy3BO06dmXwQ9OJiA4FLVGzU9ffOJQypCroJtvbiy5owJOnz6dPn365Ltt7dq1bxhTUUg1gSguR49C3762uQV27IAGDZwdkSgvJBlwM6dPn6ZmzZr238+cOYPVaqVGjRpoNBoURaFmzZrUrVvXYT+D2YL5XFyJx9dtyN28PeV+zhw+wPZff6Jmw8ZUq1Pvhvv8uf43KlStzv/N+dohuVk6571biqFWrVoAaLVaevbseUvHKAppQCiKw44dtq6D1arZxhOIiHB2RKI8kW8xNzN3rmPXvzlz5gDQr18/hg0bhkaj4ZVXXsnzpOqpUZGZ4tiboCTc0bk7/kHB/PTlXI798yddBt64VAD+K3W4NuZTB/dx6sDeW4ohPDycbt26MW/ePGJiYvKsj4+Pv6XjFkRKBsTtWrkSevaE5s1h2zZJBETpk5IBNxMVFcWgQYPo27cvf/75J4sWLWLMmDE0+3dw8tdff53nnnuO8+fPM2TIEPz8/IiKiuKnn36ixz330uu+km2J5KHV0rH/YNYu/ha1RkPnu4bcdJ9W3Xry14Y1zJ76AC269iQu+iK/L11Ildp1yc78by4FnabwQybNnTuXTp060aRJEyZOnEitWrWIjY3lzz//JDo6moMHD97K5eVLSgbE7fjsM3j0UbjnHliwAP7t/CJEqZJkwM0sXbqUl19+mRkzZuDh4cHUqVN599137etnzJhB3bp1+fDDD3nllVcAqFq1Kr1796b3gIFYoMSnL+425B7WLv6WJu06ERRe4abb3zlsJCkJ8fy+dCEHdmylSu06PD57DrvW/cbRv3fZtwv01BY6hoYNG7Jnzx5eeeUV5s+fT2JiIuHh4dxxxx28/PLLt3RdBZEGhOJWKAq89BK88QY8/jh88IGtrYAQzqBSpHzTLcyaNYtXXnmF+Ph4QkNDb+kYl9IM/BOTUryB5eP8iaM8PaSXwxgGt0sFNAj1pX6IX7EcrzgtW7aM48ePM3PmTGeHIgpJURTSTTkkZ5tJyTaTnG0m22LFalVQALUKPNRq/D09CPLSEuipJdBLi05TPHdrsxkeeQS++QZmz4bp0+EmHW6EKFFSMlCOBOsL/2R9OzYsW4yXtw/tevUvtmMqQIhed9PtnEFKBtyDoigkGsycS8nkSka2fURLFQWVlllJN+VwOf2/YbR9dRpqBfpQzV9/y4lBZiaMGAG//w4LF9oGFhLC2SQZKEd8tB6E6nUkGkwlUlXwz6bfiT57ij+WL6bvmPvxKsYB1L091IS6aDKQ22bAarVK+wEXZLZauZRq4ExKJhkmS56bf1E+CxkmC4fi0jgSn0Y1f29qBXoT6FX4JDs+HgYMgGPHbAMK9e5dhJMLUYIkGShnIoN8SDCYSuTYX7/+IqmJCdzRpTujpk0v1mNHBvncdOAiZ8ktGbBYLJIMuJjL6Qb2XU3FbP3vll8cibBVgQupWZxPzaKav56m4f43LSmIirKNKpiaClu3QosWxRCIEMVE2gyUM1ZFYe3ZOIwWq7NDKTQ10K92BTyLqb62uP3222/s3buX//u//0Ov1zs7HAEYc6wciEt1KOIvKSpAp1HTsmIAFX3zn4xr/37bqIJ+frBuHURGlnhYQhSJa367ihKjVqmoFeg+85+qgCr+Xi6bCMB/JQPXzpoonCcmI5vfo+K4UgqJANhKGowWK7suJ7MnJoUcq+Pz1YYN0KWLbTChnTslERCuyXW/YUWJqR3kg5cL31yvpVZBw1DX60FwrWurCYRznU/N4s/LyZj/7RVQ2i6mGdhxKRHzvyVvixdD//7QuTNs2gTh4U4ISohCcI87gihWWo2alpUCnR1GoTQJ98db69pNW6RkwDWcS85k39XUm29YwpKzzWy9mMgHH1kZO9bWW+CXX8DX19mRCVEwSQbKqQo+ntQI0OOaTfJs1QOheh01A1y/SkNKBpzvYmoWB+LSnB0GYKs2SM3OwVgpiRdeUvjmG9CWTq9eIW6Zaz9yiRLVJMyfqxlGjBarU4pUb0StglaVAly2B8G1cpOBnJwcJ0dSPiUZTOxxgRKBa6nUULeZmaqdU1CpgpwdjhA3JSUD5ZhWo6ZDlWDULnbDVQFtKwe5fPVALkkGnMdiVdgTk+KSJVwqFUSnZ5daQ0YhbockA+VcoJeWTlWCUbvAt6miKCiKQotwPyr65N9FyxVJNYHzHE9MJ8NscbmSrWvtu5riVl15RfkkyYAgxFtH56ohaFQqpz5hqVRwefdmdqz5xa1urB4ethIMd4q5LEgymDiVlHnzDZ3MbFU4GOta1RhCXE+SAQHYxv3vVi0Eb23pj7NvG7RFRacqIdzVuR1RUVGsXLkSq9U9nqakmsA5DsSmuWT1wPUUbNUFiVklM/KnEMVBkgFhF+ClpWeNMOoE+QCU2hdtFT8vetcMJ9zHk8jISO6++26OHz/Or7/+ijsMkJlbMiDJQOlJzjaTYjS7dPXAtVTA2RTXL8UQ5ZckA8KBRq2iSbg/XUuhlECnUdGuchCtI4IcxnWvX78+Q4YM4cCBA6xfv97lEwJpM1D6ziVnukWpQC4FuJyeTXaOvEeEa3KP5tqi1IXodfSsEcbFNANnkzNJM+XcYKrXwsnd39tDTWSQD9UDvAuc3KVp06YYjUbWrFmDp6cnd955522cuWRJm4HSZbJYuZRucJtSgVwKcCHVQL0QGX1IuB4pGRAF0qhV1Az0pkeNULpWC6GKn5fD09jNnsyuX1/Bx5OOVYLpUyucOsG+N53lrXXr1vTo0YNt27axa9euW7mEUiFtBkrXpTQD1mLKBI78tYvh9SM48lfpvL/OpmS6fEmXKJ+kZEDclEqlIkSvI0Svo4VVIdVoJjk798dEptni8OWsVoGXh4ZgLy1BXloCvbQEemrR3sJ8CJ06dcJoNLJhwwY8PT1p2bJlMV5Z8ZCSgdIVn2V0dgi3LDvHiiHH4jZjaIjyQ96Rokg0ahXBeh3Bep3DckWxTQyjgmIfNbB79+4YjUZ+++03dDodTZo0Kdbj3y5JBkpXcnbxzQHRsHU7fjh4Dg+t7uYbF5PkbLMkA8LlyDtSFAtVCY5RoFKp6NevHyaTiZ9++gmdTke9evVK6GxFJw0IS4/JYsWQU3xdTtVqNTrPmw9wZTRk4am//XkyVEBKtpnKfvrbPpYQxUnaDAi3oFKpGDRoEPXr12f58uWcO3fO2SHZaf+dhUaSgZKXcgulAomxMcx94Ske6nwHI5vUYHKPtsybNQOzyZRvm4GXxw3niYF3cvbIIV4cO5TRzWux+MO3AchMS2XOjCcY16oe41rXZ86zjxN1/AjD60ewaeXSm8aiULwlG0IUFykZEG5DrVYzbNgwlixZwpIlS7jvvvuoUqWKs8OSaoJSlGYqWiPNpNirzLjnLjLTU+k1YiyVa9YmMS6G3etXY8o2FLhfekoyrz98L536D6bLwOEEhoaiKApvT7mfE/v+pveocVSuVYe//1jHnBlPFCmmVKM0NBWuR5IB4VY8PDwYOXIkixYtYvHixYwfP56KFSs6PSbAbUZMdGc5VmuRurgu/uAtUhLieGvpamo3aWZfPvqx/7thq/6U+DgmzXqH3qPG2Zf9vXEdx/bsZtwzLzLkwSkA9Bk9npnj7y7SNViKqyuEEMVIqgmE29FqtYwePZqgoCAWLVpEYmKiU+ORkoHSYynCfdRqtfL3xnW0vLOXQyKQ60YNXbU6T+4cNtJh2b6tm9B4eNBn1Hj7Mo1GQ/+xDxQ+KMAiXQuFC5JkQLglLy8vxo4di7e3NwsWLCAlJcVpsUgyUHqK0kg1LSmRrIx0qtWpX+TzBFeoiFbn2MMg/ko0QWHh6H18HJZH1Iws0rFdbMZwIQBJBoQb8/b2Zty4cajVahYuXEhGRoZT4lCrbR8jSQZKnqaU7qQ6r5KbQlst2YBwQZIMCLfm5+fHfffdh9lsZuHChRgMBTcKK2nSZqDk6TTqQrcX8A8OwdvXj4unTxTLucMiqpAcH4ch03HCoStRZ4t0HJ1avnaF65F3pXB7QUFBjBs3jvT0dBYvXozR6JwR6nJycjCbzTIscQkK8Cx8m2e1Wk2bHn3Zu3kDZw4fzLO+qMMCt+jaHUtODuuXfGdfZrFYWLPomyIdJ8hLW6TthSgN0ptAlAlhYWGMHTuWBQsWsGTJEsaMGWPv/19StmzZwvbt2+03lVOnTvHmm2+i0WiYNm0aAQEBJXr+8ijAU1uk3gRjnprBgV1befm+YbauhbXqkBIfy671v/HG4p+LdO5Wd/amfovWLH7/TeIvX6JKZF3+2rCWrPT0Ih1HkgHhiqRkQJQZERERjBkzhujoaJYvX17idfh+fn5YrdY8T5je3t74+srMdCVBo1bhqyv8M0xIhUq8vfQ32vcewLZfV/LNGy+x5ZcfadS6PTqvoo0CqFarmfHpfDoPHMa2VSv5/qN3CK5QkWlvf1Sk4wRKMiBckEqRKbREGXPmzBl++OEHGjRowLBhw+wN/IqbxWJhzpw5pKamOizv168fbdq0KZFzlgdHjhwhMTGRkJAQQkNDCQ4ORvdvy35FUdgZdZU4k+IyzfLjoi8xuWdbHn3zQ7pf1x0xPwNqV7jpjJ1ClDapJhBlTu3atbn77rtZvnw5Op2OgQMHFvvkSWDrY969e3d++ukn+zK9Xs8dd9xR7OcqT/755x8uXrzosCw3GVCr1ehCKlC9cx9nhHbbgry0kggIlyTvSlEmNWjQgMGDB7N//35+//33EptDvnHjxoSEhNh/79ixY4m3VSjr6tatmyd5M5lMmEwmfHx8GNitM55uekOtHeRz842EcAIpGRBlVrNmzTAajaxduxZPT0+6detW7OdQq9X06NGDZcuWoVKpaNWqVbGfo7wwmUz8/fff7Nu3L9/krWvXrva/oTkhneOJzhlX4lZp1SoifEtu/AIhbockA6JMa9OmDUajkU2bNuHp6Un79u0BW92zyWTC09Pzts9Rv359VCoVISEhxXK88iQ7O5s///yTI0eOkJSUBNgSLA8PD4cumj169KBTp07232sEenMiMaPQvQpKUniVqqw4ceWG26iAmoHeaNSu0c5BiOtJMiDKvM6dO2M0Gvn999/x9PSkadOm/Pjjj1y4cIGnnnrqtov1VSoVYWFh1KhRo3gCLuMyMjLYtWsXx44dsze+1Gg0VK1aldatW9OoUSN27NjB5s2bAejVqxcdOnRwOIbeQ0MVfy+i07JdIiG4GRVQK1CqCITrkt4EolxQFIU1a9awZ88ewsLCiI+PB2D06NHUrVu30MexWBVSjWaSs82kGM0kGcwYcywYzWZUKjUeHho0KhV+Og+C9VoCPbUEeWnx1mpKpBGju0hLS2PHjh2cOHGC9H/75Xt4eFClShXatWuXp51AUlIS8+bN484776Rdu3b5HtOYY+H3qHjMbjALYJMwP+oES3dT4bokGRDlhsFg4JNPPiErKwuwFUc3b96cgQMH3nA/RVGIyzJxNjmT2Eyj/Un0ZoPfXLtep1FRM8CbmoHeeGvLR4FcYmIiO3bs4NSpU/bXXKvVUr16ddq3b0+tWrVuuL/Var1pt9DL6Qb+upJSXCEXOxW2cQW6VQsp18mgcH2SDIhywWAwMH/+fOLj4x0ap/n4+PD000/n+0Vtsli5kJrF2eQssnIsRRr5Lj+5+1f08aRWkDcVvD3L3A0iNjaWHTt2cObMGbKzswHw9PSkRo0adOzYkapVqxb7OXdfTiYmwzWrC9RAj5ph+BVhoCQhnEHeoaJcuHTpEnFxcXluvpmZmcTExBAREeGwPDrdwP6rqQ5F0Ld7s8ndPzbTyNVMI6F6Ha0qBbh9SUF0dDQ7d+4kKirKPi+El5cXjRs3pmPHjlSsWLFEz39HBX8SDSZMFqvLJQRNwv0lERBuQUoGRLkRGxvLwYMHOXjwoL3YGqBly5YMGDAAsNVD749N5UpGyU92pMI2nW3TcH9qBOjdqpQgKiqKXbt2ceHCBcxmM2Abhrlu3bp07NiR0NDQUo0nxWBk47lYUGtQucisgHWDfWgc5u/sMIQoFEkGRLljtVo5d+4c+/bt4/jx4wQEBPDEE08Qk5HNnpgUcqxKqT9hhnnraF0pEC8PTSmfufBOnjzJ7t27iY6Otnf78/Pzo169enTq1MlpEzMZDAaWL19ObGoGdXoNRlEVfprjkhIZ6E3TcH+3SvBE+SbJgCjXsrOzbQ0ETQp7r6befIcSogK8tRo6Vw3BW+saCYHVauXo0aP8888/XLlyxT7xU0BAAA0bNqRDhw5Om5DJZDJx6tQpjhw5wsmTJwEYOXIkETUj2X4pyalVBvVDfGkQ4iuJgHArkgyIci8qJYv9sc5LBHKpAE+Nmm7VQ5zWjsBqtXLgwAH27NlDbGwsVqsVgKCgIJo0aUL79u3x8nLOKHqKonDkyBGOHDnCmTNn7LEBBAcHM23aNACycyzsu5rK1cySr+rJpQI81CpaVAygsl/RZkMUwhVIyxZRrl1KM7hEIgC2BoZGi5Vtl5LoVi2k1KoMcnJy2LNnDwcOHCAuLs7e2yI0NJSmTZvStm1b+0RBzhQfH8/KlSvzXXftUNNeHhraVw7iUno2B2JTsZRCtU+ErxfNK/jj6cLVPELciCQDotxKyTazJybF2WE4UACD2cLuy8l0LcG+6SaTib/++otDhw6RkJAA2EZSDA8Pp3nz5rRq1QoPD9f6eggPD6dLly5s27bNYblGo6FevXoOy1QqFdX89YR56zgYm8aVjOzb7hrqcPx/j+WlUdOsgr+UBgi351qfdiFKiVVRXC4RyKUASdlmzqZkFessd9nZ2ezatYsjR46QnJwM2AZeqlSpEq1ataJ58+Y3HeTH2dq0acPOnTvt7RdUKhV169YtsORC76GhXeUgMk05RKVmEZWShdmqoFitt9TrIDcJCPPWERnkQ0WfsjdWhCifJBkQ5dLJxAzSTDk339CJjsSnUdHHE98b9FOPiYnBw8ODsLCwfNcXZh4AV08AcmVkZDB37lwsFgs1a9YkKioKRVFo3LjxTff10XnQOMyfBiF+HL8cyz8nzxNatQbmf2dxL+h2nluSoFZBoKeWUG8dNQK8b/g3EcIdyTtalDsp2WZOuMH0t4oCe2JSCqwuOHXqFEuXLiUsLIxHHnnEvjw1NdU+D0BGhu06PTw8qFmzJm3bts1TpO4OMjIy+OSTTzAajfTr14/WrVvzxx9/cPToUerUqVPo42jUKmJOHCZ6927GdXsOi0pNSraZlGwzJosVi6JgVUCjBo1Khb/Og0AvLX46DykBEGWaJAOi3DkUl+bsEAolt7rgckY2Va6rkz5x4gTLly/HarUSGxtLVFQUhw4dyjMPQO3atQs1D4ArS0tLY+7cuZhMJvr370/r1q0B22yGPXv2LNJNOiUlhV27dqHRaPDw8MADqODjSQUfmXpalG+SDIhyJd2YQ4LB5OwwiuRscqZDMnDs2DF+/PFHhzkWFixYANjmAahfvz4dO3akSpUqpR5rcUtNTeXTTz/FZDIxcOBAWrRo4bC+KIlAZmYm8+fPB2zdFBVFkad9If4lyYAoV86lZhZrq/LSkGgwk2Y04++pZdu2bWzevDnPNnq9nvHjx1OhQgUnRFgyUlJS+PTTTzGbzQwePJjmzZvf8rFMJhOLFi2yt5uwWq2kpaU5bdREIVyNJAOi3MixWjmfYnCrRCDXzuNnufT3NmJiYvJdbzAY8Pb2LuWoSk5SUhKff/45ZrOZoUOH0rRp01s+lsViYcmSJcTGxjosj4mJkWRAiH9JMiDKjcvp2VjcdMDNTK03gUFBRERE4OPjQ0ZGBvHx8SQkJGAwGADbzc3Pz8/Jkd6+xMREPv/8c3Jychg+fHihegvcyNq1a4mKinJYplaruXr1KvXr17+tYwtRVkgyIMqNBIPJLaoIrFYrW35ezl8b1hB1/AgZqSmEV6nG2NGjefm5Z/MMB2w0GklLSyv1mQJLQkJCAvPmzSMnJ4d77rmHhg0b3vYxAwMD0ev19qQJbK9xQaUsQpRHMjeBKDf+iIp3+bEFAAyZmYxtWYe6zVrS8s6eBASHcurAHrb8vJwuXbqwadOmMtnwLS4uji+++AKLxcLIkSOL9aldURQWLFjA+fPniYiIICYmhooVK/Lwww8X2zmEcGdSMiDKBYtVId0NEgEAD62WN77/hfotWtuX9R5xL3UiazHv3bfYuHEjPXv2dGKExS82NpYvv/wSq9XKqFGjin0sBJVKRVxcHHq9nokTJ9pnqxRC2LjH0GNC3Kb1m7fwzN39GNW0JlN6tef3JQtZOuc9htePsG+zacUSZo6/h/s7NGFkkxo8fldX1v3wXZ5jnTl8kFcfHM2Edo0Y3awWk3u0Ze7zT9rXx0VfYnj9CH75+jPWLv6WyT3bMbp5LV59YBQJMZdRFIXln37IxK4tGd2sFm9PmUB6SrJ9f61O55AIgK1qo+WdfQA4fvx4Mb86zhUTE2NPBEaPHl0igyJlZWWRlZVF1apVAfDy8kKvl/kEhMglJQOizDt8+DDDB96Fb1AwI6Y+hdViYekn7xEQ4jiE7/olC6hauy6tu/dGrdGwZ/MGvnzlORSrlX733g9AamICrz00Gv+gYIZOnIqPvz9xl6P5a8OaPOfd/ttKzGYz/cc+QEZqCj9/9SnvP/EIjdt15Ojfuxj60BRiLp5n7aJvWDD7VR5988MbXkf0v3XcZaFtQK4rV67w9ddfoygKY8eOLbHBkfbs2QPAHXfcUSLHF8LdSTIgyryXX34ZRVF4fdFPhEXYBuJp1/sunhzU3WG7VxeuwNPrv6fF/mMf4LWHxvDr/C/sycCJ/f+QkZrCS1/9QO0mzezbjnni2TznTYy9yifrd+Lj5w+A1WJh5RdzMBkNzP5xHZp/ZwVMS0pk268/8fCst9HqCh4J7+evP8Xf359+/frd4ivhWqKjo/n2229RFIVx48ZRs2bNEjvX8ePH7ZMaCSHykmoCUaZZLBbWr19Pz/4D7IkAQJXIOjTv1M1h22sTgcz0NNKSE2nUuj2xly6QmW4bwtjHz9Yvfe+WDeSYzTc8d4e+A+yJAECdZran0i4Dh9sTAdvyFuSYTSTGXi3wWCs+/5hDu7bzxptvEhgYeOOLdgMXL160JwLjx48v0UQAbI0Tg4OD3WZSJiFKm5QMiDItPj4eg8FAjVqRedZF1Ihk39aN9t9P7PubJXPe49SBvRiv6YYGkJWeho+fP43atKdd77tYNvcDfvvuSxq1aU+bHn3pPHBonqf60EqVHX739vX/d3nEdcttYwNkpqZC1bzXsHPNL/zwv3focfdoJk+eUviLd1Hnz59nwYIFqFQqJkyYQLVq1Ur8fFar1S0naBKitEgyIMoF9U264l29eJ5ZE0ZSuVYkE56dRUilCDy0WvZt3cRv332BYrW1PFepVDzz8ZecOrCXfzZv4OCOLcx94SlWzZ/HW0t+Q+/j89851Zr8YylgeX6t2w/u3MrHzz5Oi649mDTrHdRu3qMwKiqKhQsXolKpuP/++0tl/oS9e/cC0KZNmxI/lxDuSpIBUaaFhYWh1+u5eO5snnVXzv+3bM/mDZhNRmZ8Ot+hOuHIX7vyPW7d5i2p27wl9z45g+2/ruSjZ6ayc83P9Lzn3mKL/dTBfcye9iCRjZvy9Efz8NJp3Xp8gbNnz7J48WJUKhUPPvggERERN9+pGJw/fx5PT08ZeliIG5AKNFGmaTQa+vTpw/rVvxJ/Jdq+PPrsaQ7s2GL/Pbcu+dqH88z0NDavXOpwvIzUlDxP8DUa2IbLNZuKbzbE6LOneXPSOMIqV+X5zxfg6aUn0EtbbMcvbadPn2bx4sWo1WoeeuihUksETCYTGRkZVK5c+eYbC1GOScmAKPNeeeUV1q1bx0tjh9Jn9HgsFgtrF31D1dr1uHDyGADNOnbFQ6vjrcnj6T1yLNlZmfyx/HsCQkJIjv9vgpstPy9n3fff0bZXXypUrUF2ZgYbli/G29ePFl17FEu8howMXntoNJlpqQx+cDJ7t25EBVTw8eSCv57IyEjat29fLOcqDSdPnmTp0qWo1WomTpxYqjMr5nYpvJ0ZD4UoDyQZEGVe06ZNWb9+PQ9PfYwlH79HSMVKjJw6neT4WHsyULlWbab/7wt++N9sFsx+jcDQMPqMvg//oBDmvvCU/VgNW7fj9KH97FjzC6kJCXj7+VG7SXOeeHcuFaoUT0O49JRkEmKuALDo/TfzrB8/frzbJAPHjx9n2bJlaDQaHn74YcLDw0v1/EePHkWlUtGoUaNSPa8Q7kbmJhDlxpH4NE4nZdonKlo65z2Wzf2AFSeuODWuwupbKwxvrfvk78eOHWP58uV4eHgwadIkpwyW9Prrr+Pv789jjz1W6ucWwp1ImwFRbkT4ern8jIUF8dN5oPfIvxeCKzpy5Ig9EZg8ebJTEoHLly9jsVioU6dOqZ9bCHcjyYAoN4K8tPh7us+T9bVqB/m4TU+CQ4cOsWLFCrRaLVOmTCE4ONgpcfzzzz+AdCkUojAkGRDlhkqlonagz803dDEalYqq/l7ODqNQ9u/fz08//WRPBIKCgpwWy7lz59DpdISEhDgtBiHchSQDolyp4q9H8+8T9shp012+vYAKqBGgx8MNhtHdt28fq1atQqfT8eijjzp12OScnBzS09OpVKmS02IQwp24/jeMEMXIQ60iMsjb2WEUSa0g1y/N2LNnD7/++iuenp48+uijTh/gZ//+/QA0a9bsJlsKIUCSAVEONQjxw0erwR1q4BuE+uKnc+12Dn/99RerV6/G09OTqVOn4u/vf/OdStjhw4cBaNKkiZMjEcI9SDIgyh2NWkWrSoEu3bNABQR4elA32NfZodzQ7t27WbduHV5eXkydOhVfX9eI9+rVqwQEBODh4dqJlBCuQpIBUS6F6HXUdvHi91aVAm86wZIz7dy5k/Xr16PX65k2bZrLJAJxcXGYzWYiI/POVCmEyJ8kA6LcahTqh5/OwyWrCxqH+RHg6bpzEWzfvp0//vgDvV7P1KlT8fZ2nXYYf//9NyBdCoUoCkkGRLmlUavoXDUYvYdrtR+oE+Tj0qUWW7duZdOmTXh7e/PYY4+5VCIAcObMGTw8PEp1DgQh3J0kA6Jc8/LQ0KWa6yQEtQK9aRzm57IDDG3evJktW7bg4+PDtGnT8PJyrfEPcnJySE1NpWLFis4ORQi3IsmAKPe8tR50qx6Cr865w/3WC/GlWbi/yyYCmzZtYtu2bfj6+vLYY4+5XCIA//UiaNq0qZMjEcK9SDIgBLYSgjurhxLhaftIKIq1VM6rAnRqFe0igmgU6rolAhs2bGD79u34+fkxbdo0dDqds0PK16FDhwC44447nByJEO5F+t0I8S81cHTjbxjUOkKbtUPn7QMlfHOu7OdFswoBeGpcNy9fv349u3fvxt/fn0cffdRlEwGAK1eu4OfnJ10KhSgi1/0GEqKU7dy5k+joaNKvRsOZ/UQG+aAu5lwg93DeWg3tIoJoExHk0onA2rVr2b17NwEBAS5dIgCQlJSEyWSiZs2azg5FCLcj6bMQQExMDJs3bwbAZDLRt3dvAgMDaBDqx8VUA2dSMskyW1DBLQ1WlLtfRR9PagX5EO6tc9kqgVyrV69mz549BAYG8uijj7r807Z0KRTi1rn2p1uIUpCQkMD8+fNRFNttXq1W2yfZ0WnU1A72ITLIm/gsE5fTs0nKNpFmzLEnBfnd0nPXadUqgr20BOt1VA/wxlvr3EaKhbVq1Sr2799PcHAwkydPdvlEAOD06dNoNBoqV67s7FCEcDuu/wkXooRYLBb+/PNPNm3aZE8EgHzH1lepVIT7eBLu4wmAVVFIM+aQnG3GaLFgsYKCglqlQqNS4afzINBLi95D7fIlANf75ZdfOHDgACEhIUyZMgW1G8yYaLVaSU5Oli6FQtwiSQZEubV9+3a2bt2aZ3lhJtpRq1QEemkJ9HLdUQJvxcqVKzl8+DChoaFMnjzZLRIBgGPHjqEoCo0bN3Z2KEK4Jff4pAtRAho2bJin+FulUuHn5+ekiJzrxx9/5PDhw4SHh7tVIgBw4MABAFq0aOHcQIRwU+7zaReimP31118AeHraiv5VKhWKouDj47pDAZeUZcuWcfToUSpWrMikSZPcIhHIzMwkKysLgOjoaHx8fFxyICQh3IFUE4hy6fTp0+zbt4+6dety6tQpmjVrhlarZc+ePQQFBTk7vFK1ZMkSTp48SaVKlXjooYfcIhEAWLhwIbGxsYSFhWE0GomIiMBqtbpN/EK4EpVybcspIcqBrKwsPvvsM0JDQ7l48SI6nY5nnnkGtVpNamoqPj4+btF6vjh8//33nD59msqVK/PAAw+41Y00N/Zr6XQ6OnToQNeuXZ0UlRDuyX0++UIUA0VRWL16NTk5OZhMJqxWK8OGDbPfBAMCAspNIrBo0SJOnz5N1apV3S4RAKhUqVKemE0mEwaDwUkRCeG+3OvTL8RtOnLkCMeOHeOOO+7gypUrVKtWjTp16jg7rFK3YMECzp49S/Xq1ZkwYYLbJQIAFStWxGr9bw4JlUpFnTp16N27txOjEsI9lY9HICGAtLQ01qxZQ8OGDdm3bx9qtZrRo0c7O6xSZbVaWbBgARcuXKBGjRqMHz/e2SHdskqVKjn8XrlyZe655x63TGyEcDZJBkS5oCgKq1atQqvV4uHhgdFopEePHk5vfZ6dYyEl24zRYsWiKCiKbQwDD7UKX50HAZ4eqG9j0KITJ06wd+9ehg0bhqenJ/Pnz+fSpUvUqlWLcePGFeOVlL6AgAB7D5Dg4GDGjBmDVlu2xn0QorRIMiDKhT179nD27FkGDhzIr7/+SkBAAJ06dSr1OFKNZq6kZ5Ocbf539MIbT5WsAvx0HgTrtYTodVT288KjCE++O3bs4PLly8yfPx+NRkNMTAy1a9fm3nvvvc0rcT6VSoVarcZisTBhwgT0er2zQxLCbUkyIMq8xMRENmzYQKtWrdi1axdAqVYPWBWFy+nZnE3OJCnbXKTJjhQgzZRDuimH86kGDsSmUSNAT61AH/w8b/zxTU1N5fLlywDExcUBuHwiYFUUUo05pGSbSTflYLEqWBQFlQo0KhWeGrV95EeN1QJA165dy+1AUUIUF0kGRJl28eJFVq9eja+vLyEhIezZs4dGjRpRoUKFEj+3VVE4nZTJ6aQMTFbFPqHRrfTlzd3HoiicS8nibEoWoXodTcL9CPLKf1rho0eP5lmWlZWF0Wi0D7TkClKNZs6nZpGQdfMJoOC/10KDlSodexFSuyY5VmuRSkyEEI5knAFRps2ePRuDwUCdOnU4d+4carWa//u//yvx7oOp2Wb+uZpCmjGnxM6RW8JQL9iH+iF+aNSOt8/PPvvMXiJwraZNmzJ06NASi6swbqe05FqKYkWlUqNRqagZ6E3NQG/8dPKMI0RRyadGlFlXr1619znPHZymRYsWaDQlN42wVVE4mZjBicSMEjtHrtyb58mkTC5nZNO6UqC9lCAhISFPIqBWq4mMjHTq+P2KonApzcChuLTbLi0BUKlspQEWReFsciZnkjOp6ONJ8woBbjNdtBCuQEoGRJmUk5PDF198QXx8fJ51NWrUYPTo0eh0+Rev3yqTxcqu6CSSss3FetzCyH2yblExgOr+eubOnUtiYiIqlYr69evTsGFD6tSp49TqAUOOhf1XU7maaSzR86iw9choVsGf6v56t5tCWghnkJIBUSZt3ryZxMTEPMtVKhVXrlwhKyurWJOB7BwLOy4lkW4quWqBG8nN6PddTWX/wUMkJibSpEkTBg4c6PTudrmlAQdi07CUwrOHgq2kYN/VVKLTDLSoGCilBELchJQMCJdisSqkmWzd7lKyzRhyrFisClZFQaP+t/+91oNALy1BXlp8tJo8T34XL17k22+/pXXr1vzzzz8O6xo3bkyfPn3w9fUttphNFivbLiaSbsq55eLu4hZmTqdz47rODgNFUTgSn87p5EynnF8FaDUqOlcNIcBTxiAQoiBSMiCczphj4XyqgUtpBocbakGNylQY/2tRrlIRrNdSI8Cbyn5emE0mfvrpJ6pWrUrFihXt+wQEBDBo0CBq1apVrLFbFYVd0UkulQgAxGv9iE4zUMXfeX3vFUVhf2wq51OdN1eAApgtClsvJtK5anCBPS+EKO8kGRBOoSgKSdlmziVnEp2ene+NtKCb67XLLYpCfJaJ+CwTB2NV5MRfxmiFcUOGsGrVKgCaN2/OXXfdVSI9CE4kZjiljUBh7L2aSrBe55QickVROBiX5tREwB4LkGNV2H4piW7VQvCXEgIh8pBqAlHqMkw57L2aQqLh1ruUFUSxWlGpVFT0VLHx+2/p1qUznTt3LsYz/Ccl28zmCwkuVSJwLRUQ5q2jY5XgUm9EdyopgyPx6aV6zpuxVRmo6VkjFC8PaUMgxLUkGRClRlEUzqZkcSQ+DUUp3iQgv3NZsw10qR1BBd/iLyq3KgobzyeQ4WLVA/lpUTGAGgHepXa+VKOZTeddM0lSAZV8PWkbESS9DIS4hgzZJUpFpimHrRcTORSXhrWEEwGw9RrQ6PXsvJzCwdhULNbiPePJxAyXaydQkIOxaWTnWErlXFZFYU9MSqmc61YowJUMI5fTs50dihAuRZIBUeJSss1supBAcqnXrdue/M6mZLEjOhHzTSYFKiyLVXFa6/hbYVEUolKySuVcp5IySDW6fpK0Pza11BIkIdyBJAOiRCUZTGy9mEiOVXHqDSLJYGbbpURMxZAQRKcbyCnmkoaSdi4lC2sJ1whmmnI4nlDyIy8WhxyrwuG4NGeHIYTLkGRAlJhUo5kd0UlYFOcmAvDv7H/GHHZFJ932jfxscuk8ZRcno8VKTEbJFo2fK6XSh+KgANHp2RikdEAIQJIBUUKMFis7LiUVe1397VCApGwze2KSuZV2s7NmzUKlUnHx6tXiD+46w+tH8OWrzxfrMc+WYNWGxaoQlZrl9KSvKBTgQqr7JDBClCRJBkSJOBibislidcmbw5UMI9G30YDM3dqg7926kaVz3iPBYMZYTO0mrueOVSdgK+Up6eoTIdyBJAOi2F1Jzy5wICFXcTsNyFz5uvKzb+tGls39ALA15iwJZ9ygQeXw+hEsnfOe/fdNK5cyoE5F/jl6yolRCeEaJBkQxcposbLvaoqzw7gpi9U2VG5RqgtudUgOq9WKyej8rmwqSiYZMOZYSTU6Z4Km4hBvKNlZFIVwB5IMiGJ1LCEdsxsUFytATIaR2CJMp5t7XWnJSbz3xCTGtqzL+LaN+PqNlxxu9rn1/dt+XcnjA7oxqmkN9m/fDMAvX3/G86MGMr5tI0Y3q8Uzw/rw57rfCnX+Hz/7iLsbVGbNwq/ty/Zt28SL9w5hzB2R3NuiDm9MGsfF0yft6+fMeIJ1388HYFj9COqH+hX7YDspRtccjvl6Pxw8x92Tn8izvKRKS4RwJzI3gSg2ZouVC27UiEyFrXi7oq9XobbPrVZ4/4lHCK9chXufeo5TB/exZuHXZKal8tg7H9u3PfLXTnat+5V+996Pf1Aw4ZWrArB64Ve07t6bzgOHkWM2s3PNL7z3xMM8//kCWnbrWeC5v//oHVbO+5hJr8ym14h7Adjyy498MuNxmnfqxtinX8CUbWD9Dwt48d4hvLfyd8KrVKX3yLEkx13l4K5tPDZ7Dlq1iuYVAm7xFctfcnbxDytdEnSe+f+dM00WLFbbrJhClFeSDIhiczHNgBsUCtgpQFyWiQxTDr66m38UchvIVahSlRmfzgeg37334+3ry7rvv2PQA49Qo15DAK5EneWDVZuoWttxGuE563bg6fXf8Mj97r2fZ4b14df5XxSYDHz3ziv89t2XPPrmh9w5dAQAhsxMvnnjJXrcPYbJr71r37bbkBFM69eZFfM+ZvJr71LvjlZUqlGLg7u20XXQcNTAkHqVCvsSFUpytoklc95j2dwP+N+arSz5+D0ObN+MxkNLl0HDGDf9BXSeXrw0dhiZ6Wl88MsfeY4xrW8nwipX5eWvfwAgMy2Vb96cyd9/rAWVijbd+zBgwkSmD+3No29+SPdhIwF4edxwAF5duMLheHNmPMHRv3fx+aa/7cuG149gxKNPMXLadIdtFSDNZJYZDUW5JtUEolgoiuIWjciup4JCj86Xm+f0HTPBYXm/sQ8AtoZ6uRq2bp8nEQAcEoGM1BSyMtJo0Kot544dzveMX776PKsXfs1js+fYEwGAQ7u2kZmWSqe7hpCWnGj/UWvU1Gl6B0f+3pXvNVi59bYPBbm2mP39Jx7BbMzm3qeeo0XX7qxZ+DWfv/x/AHQdPJwLJ49x8dQJh/3PHD7AlfPn6DLIdmNXFIW3p9zPtlU/0mXQMEY//n8kxsYwZ8YTxRq34zW4b5sHIYqDlAyIYpFgMJFpLnrr/Ee6t6FRmw5Me/ujQu8zZ8YT7P79NxbvO3PD7Qp6aryWAkSlZtEw1C//YuK0NHjvPRg8mNx7aKUatRw2qVi1Bmq1mvjL0fZl4VWq5nu+PZs38OPn/+P88aOYTf+1V8ivHn/Lzz+SnZXJw7PepvOAoQ7rYi6cA2DWhHvyPY+3r1++y8F2zcVZIG6y/Jdc3KjUpH3fgXz9+kts/XUF455+wb7P1lUr8PL2pl2v/gD8s2k9x/bsZtwzLzLkwSkA9Bk9npnj7y7GqP+jgmIbqloIdyXJgCgWcZkml683Toq9yoZli2jTsy81GzS2L8+xKqQazQQv+g4+/xxGjICRI+HcORg3Dq5cgaQkVD7++R43vxt5fvXTx/b8xdtTJtCwVTsmznyToLAKaDw82LxyKdt/+ynP9vVbtOb8iaOsXfwtHfoOxC8wyL7OarXdvB6bPYfA0LA8+2o0BX+01cXcgPDafvr5lZqs+/479m3dyLCHp9G6R292rP6ZsU89j0qlwmKxsGvtKtr06IuXt21mxX1bN6Hx8KDPqPHXXI+G/mMf4Piev4o19lwWGWtAlHOSDIhikZxtuqVEYM667ahUJVNb9dK/9c+5kuJiWTb3A8IqV3VIBsDWCC743DnYtw8OHIAZM2wrcm+cZ86grt8IgJjz56hQpZp935iLUVitVsIqV7lhPLt/X43W05OXvv4erc7TvnzzyqX5bl+xWg3GPfMiM++7m9cn3susb5eh9/W1rwMICA6hWYcuNzzvtcmKpoTbyN2s1KTr4HvYuWYVx/b8RaPW7Ti0azspCfF0GfTfU3/8lWiCwsLR+/g4HCuiZmSJxS2pgCjvpM2AuG2KotzyjIRanSceWm0xR5R7bB1a3c0bhdn730dE2BZYrykyzn1iXL8e3Y/LAexd9XKtXfQNAC26dL/hedRqDSqVCqvlv+qUuOhL/L1xXYH71KjXkBfmLST67GnemjweY7YBgOaduuHt68fKeXPIMed97VOTEu3/9/z3iTszLRWfQjSULKobNcK/vtSkeaduBIaGsW2Vrepm268rCAwLp2mHzrd28gJKOazWolVZaYq5tEQIdyPJgLhthhxrvmMLHN/7F/93dz9GNa3JlF7t+X3JQpbOeY/h9SPs2zzSvY1Dw7Acs5lln7zPo306MqppTca3bcQLYwZzcOfWG8YQdfwI97dvzMvjhmPItDVkfHnccHu7gSN/7eLZe/oBMPf5JxleP4Lh9SPYtHKpbc6CQ0fgscdueA6vzp0AiI2+xFuTx7Pu+/n87/+mse777+g8YCg1/i05KEjLbj0wGgy8NvFe1i9ZwLK5HzBj5F1UrFbzhvvVbd6SGZ9+y6mD+3jv8YfJMZvx9vXj4ZlvcXzvXzwzrA8/fv4/fl+6iO8/eofpQ3ux7JP37ftHNmoKwNdvvMTu1T+zZMmSG56vqDTq/75GYs6fc1h3famJRqOh011D2f37ajJSU/j7j3V06j8EjUZj3ycsogrJ8XH2v2OuK1Fn85zbNyCAzPTUPMvjr0TnWVYQBfCQboWinJNkQNy21HwGnblw8jivPjia1KQERkx9iu7DRrL0k/f464+Cn4IBln7yPsvmfkDjNh146KXXGf7IY4RWqlxAa3ubM4cPMGvCCGo2aMwLXy7KU7wMUCWyDqMeewaAXiPG8tjsOTw2ew4NW7cDIL16TawffJD34NcWsVeqCMDTH36OVufJovffZN/WjfS7936mvPF+3n2v06RdJ6a88T4p8fF8++ZMW9350y/QtmffQu379Eefc3DnVj5+dhpWq5XOA4cx89tlBFeoyC9ff8a3b77MzjW/UKN+I7oPG2Xft22v/vQf+wD7t2/m5WmTGD169E3PVxQBnv+VNhSm1KTr4OFkpKbw+cxnyc7KpOugYQ77tOjaHUtODuuXfGdfZrFYWPPvsa5VoWp1Lp8761AScv7EUU7u+6dI1+DvKTWmonyTT4C4bflNULNkzrugwOuLfiIswvZU2K73XTw56MZF6fu2/kGLLj0c+s7fyIl9f/PGw+No0LItz8z50qEu/lqBoWHc0bk7Sz5+l7rNW9L1325sdio1lkcfRT19uq2aICQEnn3W1nhw8WKIj2fWxIn0m/4Sl9Ozmf6/LwqMacWJKwWu6zF8ND2G570ZX9/3Pb9jtO7eh2VHLjosa9y2A43bdijwfGB7Gn/wxdd58MXXubN6KEFexVstc+3xcktN7uh8JycP7GXbqhV5Sk1qNWxCtTr1+XPdr1SJrEOtf0sucrW6szf1W7Rm8ftvEn/5ElUi6/LXhrVkpafnOXeP4aP5bf4XvPaQ7XVNTUzg96ULqVK7HoaMvNsXJNCzZKqqhHAXUjIgbtv10xRbLBYO7NhC6x597IkA2J7Om3fqdsNj+fgFcOnMSa5cV9ycn8O7d/LaQ2No0r4Tz8z5qsBEoLAsag306mXrSXDiBDzzDHz4IcTE2HoW1K9PiN59B6ZRq8C/BNoMXJsMFLbUpOsQW4PBroPydhdUq9XM+HQ+nQcOY9uqlXz/0TsEV6iYb/fTKpF1mPbOx2SlpzP/7VfYs/l3HnvnY2o1bFLo+PVaNVqNfBWK8k1KBsTtu666NS0pEVN2NpVq5K0Lj6gR6TA4z/VGPvYM7zx6P9P6dqJanfo079yNroPvto/sl8tkNPLmI+OIbNSUpz+ch8bj9t/KKoB1+VRjaDRQ03YtVf30HI5Lc7vW5yqgur93iQy5G3hNMuAfHHzDUpNcHlodKpWKzgOH5rveLzDIYXhnsDW2zE+XgcPoMtCxqiG/pPP60pbuw0bSY9hIKvsVbjhqIcoySYfFbSvOltiNWrdj7u+7ePSND6hapx4bf/yBZ4b14Y/lix220+p0tOzak9OH9tsnAbpd6kLcKD091FTx8yrWQXtKgwLUCvQukWN7e2iK1ABPURQ2/vgDDVu3dyg5cgYFCPV239IeIYqLJAPitl1/I/APDkHn5UXM+ag82145n7dF+PX8AoPoPnwUT33wGfM276F6vQYs/cSxcZ9KpeLxdz+hSbtOvP/EJI78lf/wu9fvcyOFTWoig3zcrmQg2EtLQDG3FcilUqkKNa5/dlYW23/7ic9f/j8unjrOwAkTSySeotCooKq//uYbClHGSTIgblvAdY2vNBoNzTt145+N6x26eEWfPc2BHVtueKz05CSH3/U+PlSsVpMcU96phrU6Hc/M+YrIJs14a/J4Th/af8Nje3rbvvSz0tPyrPPTeRR6ZL4gL62t7t2NRq2LDMrbw6I4BXrdvJomLSmRj6Y/yp/rf2PYpMdo3b1PicZ0MyqgeoA3WrV8DQohbQbEbdN7qNGqVQ5jDYycNp0D27fw4tih9B09HovFwtpF31C1dj0unDxW4LEeH9CNRm3aE9moKb4BgZw9cojd63+j373357u9p5ee5z9fwKzx9/D6xLG8tnAF1erWz3fbilVr4OMfwPolC/Dy8cFL702dZi2oWKUawfrCPTUrisLZs2c5v2c/wXd0KtQ+zqTC1m2upOvFX3/1VQY98iSX07MLLDUJr1L1hj0tbuZ297+eAtQsoaoTIdyNJAPittmKibXEZZnsy2rUa8iLX33Pd2/PYsnH7xFSsRIjp04nOT72hslA/3EPsmfT7xzcuQ2zyUhYRBVGP/4sgx+cXOA+3r5+vPTV97w4bhivPDiK1xf9RKXqeRsvemi1THv7IxZ98BZfzJqBJSeHR9/8kApVquXpWnbp0iV27dqFwWCw/2RnZ2P+d7S/KlWqEK6FuFsbeLFUta4UWOzzEeQnMsiH6PTsEj9PcVABwXptnlItIcorlVLc85mKculoQjqnEjNuWpe+9N9574vzCa84dKsWQvA13Qb379/PqlWr8t02PDycRx55hByrwoaoeLJdeMa7RqF+1AvxLbXz/XMlmegblA64ChVwZ/VQh54QQpRnUlkmikW4t87lbwAF0apVeZ4QmzZtSnBwcJ5tfXx8eOCBB1CpVGg1alpWCiylKItGhW1kwDrBJdtW4HrNKgSgLenZkIpB/RBfSQSEuIYkA6JYhOp1+Go1N9/Qxaiw1Rtf3//+8OHDpKXlbWg4dOhQPD3/G9yogo8njUL9SjrMIlEBOo2adpWDSqV64Fo6jZoWFQNL9ZxFocI28FJplpYI4Q6kzYAoFiqVisggHw7G5b2BujIFqBnwXyOy2NhYli1bRlJSEhqNhrCwMBISEgBo1qwZkZF5p9GtF+KL2WrlVFJmnnWlTYWtq2eXqiH4aJ3z8Y7w9aKqnxeXXLT9QKtSakMhhDuRNgOi2JgtVlafjSWfqQpckgoI9/akY9VgTCYTP/74I6dPnwagUaNGDBkyhLS0NObOnYuXlxfTpk3Dyyv/VvmKonA8MYMTiRmleAWOcksEulQNwc/JE+9YrAo7ohNJMphdqvqobUQglf1kXAEhrifJgChWB2JTiUrJcqkbwI10rBLMsb93sWPHDqxWK+Hh4YwcOdKhvcDp06fx8/OjYsWKNz3e2eRMDv1bOlLar4G/pwftKwc5rUTgemaLlW2XEkkz5rjE+6FFxQBqBEhXQiHyI8mAKFYmi5Xfo+IxuXALe7A9RfuRw/5VS8jKysLLy4tBgwbRoEGD2z52mtHMnpgUUow5tx/oTeQWdtcP8aVeiK/LFX+bLVZ2XU4i0eC8PpgqbFUDMtKgEAWTZEAUu5iMbP68nOzsMG7IajZx4rclKGYT7du3p3v37qiLcSQ6q6JwJimTowm2aXRL6kPm7+lB64qBJTbUcHGwWBUOxqVyPtVQqufNrTZpXSmQcJ/bm9FSiLJOkgFRIly9v/mFnRsI1aq45557CmwHUBwyTDmcTcnkfIoBSzF81FTYEosATw8ig3yo5q93udKAgsRlGtkTk1Jq4zJU99fTJNwfnUxPLMRNSTIgSoTJYmXDv9UFrvQGUxQrhqvRdK9blYiIiFI7b47VyqW0bM4mZ5JmslUf5N7Yb+TabdRAFX89tQK9HQZIcidmi5XD8WmcTzUU6vpvhadGTcuKAVT0lamJhSgsSQZEiUkzmtl6MRGzxQou8PSqWK14Ws30a1A9z7gCpRaDopBltpBiNJOc/d9PTj5dMHy0GoL1OoI8tQR6aQn08sCjjEyqk2Y0cy4liwupt19icm1pSe0gH6r46Z329xXCXUkyIErU+dh4/onPQuOhdW5CoCgEemnpXDUErQsWG1sV5d8f21TKatXNp1wuC3KsVi6mGYhKySL1mgaXNyo1uHadh0pFhJ+XW5eWCOEKJBkQxUpRFPtNzGq18u2332JSe1C5Y2/MFisqJz3Zhup1dKgSVGaerMuiHKuVVGMOydlmUrLNpBrNWKwKln/fUxqVCi+NmiC9raQkyFOLt1ZTLpImIUqaa3RIFmXGDz/8QEpKCkOHDuXMmTNcvnyZcePGsWn7RvR1muAVHF6K0SiAijpBPjQM9ZOiYxfnoVYTotcRIk/4QpQ6KRkQxWr27NkYDP91IWvatCmnTp0iOzubhx6aiNE7gEPxaShKyQ/K46PV0KpSoNxchBDiJqRkQBQbo9HokAgAHDp0CICwsDAqV7a13q/g48neq6kkGEwl1qJcSgOEEKLwJBkQgK0BW86/9bNga8TmoVYVqQ97cnLBAw3Vrl3b/n8fnQddqoWQZDBxLiWL6DQDt9PzXLHa2iJ4atTUCvSmRqA3eg/3m0FRCCGcRZKBcshiVUgx2hppJWebSTKYyDBb8t3W99/ubYFeWgI9tQR5aQt82j579myB5/zzzz+pXLkyjRo1si8L1usI1utoGu7PhdQsLqVlk2r8b2Kb/EsNFBSrYm+IqFKsVPDVUyNQTyVfL7cZgEcIIVyJJAPlSIYph6iULKJSs+z92m9WTJ9htpBpNnAxzVb8r1GpqBnoTc1Ab/x0jm8fs7ng8edVKlWeKoRcOo2aOsG+1An2xaoopBlz7MlKdo6FHKuCOcdC3NUYsjIzMKWnEhEcQLd2bfDVeUhrciGEuE2SDJRxiqIQm2nkTHImcVl56+gLU19/7TYWReFsciZnkjMJ1euoHexDJR9PVCoVZ86cyXd/Pz8/HnroIfz9/W96LrVK9e8AO1oIsHVPXLt2LXv37kVRFKpVq8a999yDr69vISIXQghRGJIMlGHZORb2x6YSk2G0z25XHI31co+RaDCRcNlEBR9PWlQMoEePHmzfvp2oqCgAvLy8yM7OpkmTJoVKBK538OBB1qxZg8lkws/Pj+HDh1O9evViuAIhhBDXkmSgDFIUhej0bA7EptqrA0qixX7uMeMyjWw4F09tHx+uXLlCSEgIDz74IGq1mu3bt9O8efMiHTc2NpZly5aRlJSERqOhV69edOjQodjjF0IIYSPjDJQxOVaFvTEpXM7Idsr5s+NjGNyyIT76G08Sk5mZyeHDh2nTpo196mCTycSPP/7I6dOnAWjUqBFDhgzBw0NyViGEKEnyLVuGmC1Wdl1OItFQcEO+kuYVVom/YzPoWFVX4NSxiqLw888/c+bMGdRqNW3atGHLli1s374dq9VKeHg4I0eOJDg4uJSjF0KI8klKBsqIHKuVbReTHLrmOYsK8PP0oGsBkwIdOnSIn376CQAPDw+0Wi0GgwEvLy8GDRpEgwYNSjliIYQo36RkoAywWBV2RSe7RCIAtrYE6cYcdkYn0alqCB7XjEuQkZHBmjVr7L/n5OSQk5NDhw4d6NGjh73KQAghROmRb94y4FhCOgkGk0skArkUICnbzOG4NIflv/32G0ajMc/2TZs2lURACCGcRL593VyiwcTp5Exnh1GgqNQs4rNsN/81a9Zw8uTJfLfbuHFjaYYlhBDiGlJN4MYsVoU9MSklNtlPcfn7cjLGo7s5fPAgAFqtFl9fX7y8vPD29kav11OrVi0nRymEEOWXNCB0Y4fj0ly6VCCXolhJO3+G5hX8ad68uQwfLIQQLkZKBtyUIcfCGTdIBABUKjUBNetSt1aYJAJCCOGCpM2AmzqfkuXSVQPXUwFRKVnODkMIIUQ+JBlwQ1ZF4Zyb3VgVbMmAxepOKYwQQpQPkgy4oZiMbIwWq7PDKDKzVXHaMMlCCCEKJsmAGzqfasBda97Pu1mJhhBClAfSgNDNKIpCoosNMHQjG5YtZtuqFVyOOkNmWhrB4RXo36sHM2fOpEaNGs4OTwghBNK10O1kmXNYdy7e2WEU2hevPIfRYKB63fr4BAQQF32JbSt/wGqxcPDgQSIiIpwdohBClHuSDLiZy+kG/rqS4uwwbovn1XMM6NaJt956ixkzZjg7HCGEKPekmsDNbNqyjVeef5aLp04QXKEiQx6cQnJ8LMvmfsCKE1ds26xYwtZVK7h4+gRZ6elUrFadfmMfoO/o8Q7HOnP4IN9/9Dbnjh7CaDAQGBpG47YdePTNDwGIi77E5J5tue+Zl9B5ebHq23mkJMTRoEUbprzxPiEVI/jxs4/4fekiMlKSadaxC4+++SF+gUEFxq8C/CpUBiAlJaVEXiMhhBBFI8mAGzl8+DATRwzFLziYEVOfwmqxsPST9wgICXPYbv2SBVStXZfW3Xuj1mjYs3kDX77yHIrVSr977wcgNTGB1x4ajX9QMEMnTsXH35+4y9H8tWFNnvNu/20lZrOZ/mMfICM1hZ+/+pT3n3iExu06cvTvXQx9aAoxF8+zdtE3LJj9qj2ZuFZ6chJWq5X4K5d5f97/AOjRo0cJvEpCCCGKSpIBN/Lyyy+jKAqvL/qJsIgqALTrfRdPDurusN2rC1fg6aW3/95/7AO89tAYfp3/hT0ZOLH/HzJSU3jpqx+o3aSZfdsxTzyb57yJsVf5ZP1OfPz8AbBaLKz8Yg4mo4HZP65D42F7G6UlJbLt1594eNbbaHWeDseY2LUlZpNtwqKAoGA+/vhjevXqdbsviRBCiGIgXQvdhMViYf369XTu08+eCABUiaxD807dHLa9NhHITE8jLTmRRq3bE3vpApnptimFffwCANi7ZQM5ZvMNz92h7wB7IgBQp9kdAHQZONyeCNiWtyDHbCIx9mqeY7zwxSJe+GIR45+dSXhEZTIz3WMoZSGEKA+kZMBNxMfHYzAYqFozMs+6iBqR7Nv63xTAJ/b9zZI573HqwF6MBoPDtlnpafj4+dOoTXva9b6LZXM/4LfvvqRRm/a06dGXzgOH5nmqD61U2eF3b1//f5dHXLfcD4DM1FSo6hhjk3YdAWjRpTt97hrAA7074+vry9SpU4vwKgghhCgJUjLgZm422NDVi+eZNWEk6clJTHh2Fs/PW8jL3yxhwPiHAVD+HQ5YpVLxzMdf8taSX+l77/0kxV5l7gtP8czwvhiue2pXqzX5nqug5TfroFKtZi3uuOMOFi9efJOrEUIIURqkZMBNhIWFodfruXIhChU4DDp05fxZ+//3bN6A2WRkxqfzHaoTjvy1K9/j1m3ekrrNW3LvkzPY/utKPnpmKjvX/EzPe+4tketQAXoPNQaDAaPRWCLnEEIIUTRSMuAmNBoNffr0Ycu61cRdibYvjz57mgM7tth/V6ttf9JrH84z09PYvHKpw/EyUlPyPMHXaNAYALPJVCwxW3JyyEhNcVimABeOHuTw4cO0atWqWM4jhBDi9kjJgBt55ZVXWLduHS+OHUrf0eOxWCysXfQNVWvX48LJYwA069gVD62OtyaPp/fIsWRnZfLH8u8JCAkhOT7WfqwtPy9n3fff0bZXXypUrUF2ZgYbli/G29ePFl2Lp8tfdlYmk+5sRYd+g6haux5eem8unDrOtp+XERAQwEsvvVQs5xFCCHF7JBlwI02bNmXtunU89OhjLPn4PUIqVmLk1Okkx8fak4HKtWoz/X9f8MP/ZrNg9msEhobRZ/R9+AeFMPeFp+zHati6HacP7WfHml9ITUjA28+P2k2a88S7c6lQpVqxxKvz0tPj7jEc+WsXu9evxmTMJiisAqNGjeKll16SuQmEEMJFuO1wxIqikGW2kGI0k5xtJiXbjMmiYPn3cjQq0GrUBHpqCfTSEuSlxUerQaVy1/n+/rP5QgLJ2f91B1w65z2HEQhdmb/Og541w26+oRBCiFLjViUDuTP2nUvJ4mqmkZzclvFQ4Cx+CVn/zfCnUakI99ZRK8iHcG+d2yYGVXy9SM42cfO+Ba6nqr/+5hsJIYQoVW6RDJgtVi6mGTibnEmG2ZLn5n+joo1r11kUhauZRmIyjXh7aIgM8qZ6gDc6jWu1o9y/fz8bNmzA29sbPz8//Pz88PHxwWQykZqaSnxiElV7DUOtyb9rn6tSAdUDJBkQQghX49LJgKIoXEwzcDAuzV4KADe++d/0mP/+m5Vj4XB8OkcT0mkS5k+tQG+XKSnQaDQYDAYMBgOJiYl51rdq1YoALw3x5tt7LUqTCqji54WXh3slMEIIUR64bJsBQ46FfVdTic0snb7oIXotrSoG4qNzfn5kNpuZPXs2OTk5DstVKhVjx46lVq1aJGeb2XwhwUkR3pqu1UII0eucHYYQQojruGQycCnNwP6rqVgUpdSefFWASoVTSwnS09PZuHEjx44dw3zdfAEqlYp7772XyMj/hiPefinRoU2Eq1IBQXotXauGuEzpixBCiP+4VDKgKAonkzI5lpDu1DhqB/nQJMyvVG5cVquVAwcOsGvXLnuVgLe3N3Xq1OHgwYP27YYOHUrTpk0d9s0yW9gQFW/vQeGq1CroWSMMXxcodRFCCJGXS307H0tI52SS82ezO5OcSY7Vyh0VAkosIUhMTOSPP/7g9OnTWCwWVCoV1atXp0ePHlStapvlJy4ujpiYGHr37p0nEQDw1mpoGu7P/tjUEomxuDQO85dEQAghXJjLlAycTMzgqJNLBK5X3CUEVquVP//8k3/++YfUVNsN3M/Pj1atWtGhQwc8PBxvmJcvXyY2NpYWLVoUeExFUdgRneSS1QUqIMhLS9dqUj0ghBCuzCWSgcvpBv66kuLsMPLVvII/tQJ9brhN7ktY0A0vJiaGP/74g/Pnz2O1WlGr1URGRtKzZ0/Cw8NvO8Yss4XNFxIwWawukxCoAA+1iu7VQ12iUaYQQoiCOT0ZMOZY+D0qHrPVVW5jjtQq6FUzDB9t/je07OxsvvvuO6pVq0a/fv3sy3Nycti2bRv79u0j898pgYOCgmjfvj0tW7a0TyhUXNKMZrZeTCTHWnqNLm9Eo1LRtVoIgV5aZ4cihBDiJpz+yLY/1nEMAVejKLA3JpXOVYPzPPnn5OTw/fffc/XqVeLj47nzzjuJiYlh06ZNXL58GUVR8PDwoHHjxvTs2ZOAgIASi9PfU0vnqiFsv+TchEAFaNQqOlUJlkRACCHchFNLBqLTDfztotUD12se7k+toP+qC6xWK8uXL+fkyZP2agIPDw/72ABhYWF07tyZJk2alGqc6aYctl9KxJhT+lUGKkCnUdO5ajD+npIICCGEu3BaMmCxKqw9F4vJ4rqlAtfSqKB/ZAW0GjWKorBmzRr27NnjsI1KpeKOO+6gR48eeHt7OylSMFmsHI5L40KaoVTPW9XPi2YVAlxueGchhBA35rRqgisZ2W6TCABYFLiYZqCKtwcLFy7k8uXLebZRFIVWrVo5NREA29N5y0qBVPbzYu/V1BJtWKgCtBoVLSoGEuHrVUJnEUIIUZKcVjKw5UICSdnmm2/oMhQwGTnx6w95Rge8VseOHenZs2cpxnVjZouV44kZRKVkFfvgRBoVVA/wpmGon5QGCCGEG3NKMpCabWajm42rn8s/6RKtG9bD19eXzMxMMjMzycjIICMjg8zMTGrXrk3FihWdHWYeOdbcmR+zSDfl3HDa54Lk7uOr1RAZ5EM1fz1aSQKEEMLtOSUZOBCbSlRKlkt0gSsKFVDJ14t2lYOcHcotUxSFpGwzcZlGkrPNJGWbMVmsN9xHp1ER5KUjyEtLuLeOEL1OBhESQogyxCltBmIzjW6XCIDtqTg+y4iiKLd8Mzx69CizZs1i7969XL16FW9vbxo2bMgzzzzDwIEDizfgfKhUKkL0OofZAw05FlKNZnIsir0qQaNS4aFWEeCpxctDLTd/IYQow0o9GTBbrWSaLaV92mJjtioYcqx4azW3tP+FCxdIT09n/PjxREREkJWVxYoVKxg0aBDz5s3j4YcfLuaIb07voUHvcWvXI4QQwv2VejVBQpaJbZcSS/OUxa5tRBCV/Yqv5bzFYqFly5ZkZ2dz4sSJYjuuEEIIURiFbv3l6+tLjx492L17t33Z/PnzUalUbNu2jUmTJhESEoK/vz/33XcfycnJeY6xdu1a+vXoxpg7Irm3RR3emDSOi6dPOmwzZ8YT3NuiNomxMbz96P3c26I297dvzHfvvILF4liikJmWypwZTzCuVT3Gta7PnGcfJ+r4EYbXj2DTyqX27c6fPMacGU8wuWc7RjWtyYOdmjH3+SdJT05yOJ4hI4Nv3nyZR7q3YWSTGtzfoQmvPDCSc0cP2bd5edxwurVpwaFDh+jatSve3t7Url2bH3/8EYCtW7fStm1b9Ho99erV448//rjpa6vRaKhatSopKSk33VYIIYQoboVOBl566SWioqLo1q0bf/31l8O6qVOncvz4cWbNmsV9993H4sWLGTJkCNcWOixcuJC77roLnd6bcU+/wD1TniD6zClevHcIcdGXHI5ntVh57aEx+AUGcd//vUzD1u1Z9e08NixbZN9GURTennI/21b9SJdBwxj9+P+RGBvDnBlP5In90M5txEZfoPuwkTz44ut07D+YHWt+4Y1J4xxinDfrWdb/sIB2ve9i4sw3GfzAI+g8vYg+d8bheCnJyQwYMIC2bdsye/ZsPD09GTVqFEuXLmXUqFH079+ft99+m8zMTO6++27S0/POxpiZmUlCQgJnz57lww8/ZO3atfTo0aOwfw4hhBCi+ChFcOXKFcXPz0/p0qWLoiiK8u233yqA0rJlS8VkMtm3mz17tgIov/zyi6IoipKenq4EBgYqEydOVHZfTlJWnLiirDhxRfl6x0HF289f6XnPvfZl3YaMUABl1GPP2JetOHFFqdmwsRLZqKn992fnfqMAyrhnXrQvW3b0ktKgVVsFUB5980P78u8PnHU41ooTV5Qn3/9UAZTXFv1kX+bt56/0HTMhz7bX/jRq3V4BlO+//95+vSdOnFAARa1WK7t377YvX79+vQIo3377bZ7XctKkSQq2NomKWq1W7r77biUpKakofw4hhBCiWBSpk3ilSpUYM2YMO3bsIC0tzb784Ycf5v/bu/eYKs8DjuPfc4ED53AO4IWbMqvMcrHRCjialODEDcRVtxqnQKbWZo6YZolbbNo/jMakwUnClnUWqiudN8yyZEtdMrqFZolNbcfYenNpUFtLphZUKBe5yLnuD8rBI1blIuew8/v8dd7nPOd533P+Ob/3eZ9LRMToWvQ7d+7EbDbT0NAAQGNjI93d3ZSVldHZ0UFvVye9XZ0YTUYWL13Of/757phzFZVuDTjOzMnj2pX/+o/fP/N3TGYzxaXb/GUmk4m1P3p2TFuWqGj/a+fQLXq7Onl0WQ4Alz4553/PZndw8eMP+PJa+z1/h2ibjdLSUv9xeno6cXFxZGZmkpeX5y8feX3p0qUxbezatYvGxkaOHTtGSUkJHo8Hp9N5z/OKiIg8DOOeTZCZmYnX6+Xy5dGu/cWLFwfUiYmJITk5mdbWVgAuXrwIQGFh4V3btMbYA44jLVHEzpod2KYjlr6ebv/xjS+uED83gWibLaBeysK0Me3f7O7iD6/8krMNp+npDFzsaODmaKjZ8vweDr24i4pVuSxaspTsgkJW/uCHJKUuCPjM3KSUMVPtYmNjSU1NHVMG3HX8REZGBhkZGQBs3bqVoqIi1q1bR1NTk6bxiYjItJqWqYVe7/CiNidOnKDLbKNjMPAO2GQKvAzjFK9qV/2zCs5/8C++/+xOFmY+RpTVitfr46Ud5fi8owvuPFmynqycPJreepMPz57h9Ou1vPFaDc//5jWyC0aDjMl092l4X1fue4AJGxs3bqSiooILFy6Qnp4+zm8oIiIyceMOAy0tLRiNRlJTU2lubgaG7/xXrVrlr9PX10dbWxtr164FIC1t+G49ISGBJUvzaO2Z/OqDc1Pmc+4f7zDY3x/QO/DF558F1Ovr6ebce++w+ae72fTcz0frtY7tugeIT0hkTfkzrCl/hp7ODnZvKOaPr/46IAw8jBv3wcHhHQZ7enqmvnEREZF7GNct+LVr1zh16hT5+fk4HA5/+ZEjRwI276mtrcXtdlNSUgJAcXExDoeDyspKrEbfmCDQ8+X41x3IXlmIx+3mb78/5i/zeDw0nHw9oJ5x5G79jrvzvxz/bcCxx+Oh/7ZHBgCxs+cwKyER1x3P8o2TSAPXr18fU+ZyuTh+/DjR0dFkZWVNuG0REZGJeOCegaqqKg4fPszQ0BBVVVUB7zmdTlavXs2mTZs4f/48NTU15Ofns379egAcDge1tbVs2bKFpwvzefy7T+GIn01H21XeP/MW6ctXsGNv5bguPHdVERnZK6ivruTG1cvMT3uUpsY3GbhjGp81xk5W7hO8UVeD2+1mVmISH509w/XbBiMC3Orv4yffzuGJoqd4JCOLKKuNj997m0/Pfci2F/YF1DVNIgxUVFTQ29tLQUEB8+bNo729nfr6elpaWqiuriYmJmbCbYuIiEzEA4eB/fv3k5eXx8mTJwNGzAMcOnSI+vp69u7di8vloqysjJdffjlgIFx5eTkpKSkcOPALTtfV4nY6mZWYRGbOtyjcUHrn6e7LaDTyYs1RfndgH2//+U9gMLCisIhtL+xl99NFAXV3Vb9C3Ut7+Oupo/h8PpY9uZI9R+r5ccFyf53IqGiKy7bx0dkzNDU24PN5SfrGI+zYd4A1ZdsC2jMZJx4GNm/eTF1dHbW1tXR2dmK328nJyeHgwYP+8CQiIjKdJrUc8dGjR9m+fTvNzc3k5uY+8OcaP7/BTad7oqe9p+tXLrPzO3k8V/krCjdsnvL2LSYj3/tm4pS3KyIiEixB2Yw+OcbCTJw8ZwCSbJZgX4aIiMiUCkoYWBhrnbFbGC+Kt923noiIyEwSlDBgizSTaJ15vQNxlgjioyLuX1FERGQGmfYtjEe0993i3atjV+YLZTlJsSyItQb7MkRERKZUUHoGABJtFmwRd1+xLxRFmgzMt0ffv6KIiMgME7QwYDAYyEmKDdbpxy07MW5SUwpFRERCVdDCAMAcq4W0uNDudjcA8+1RpNijgn0pIiIiD0VQwwDAkrl2rObQfVwQYTSwLHHm9GCIiIiMV9DDgNloJDc5LmRnFmQnx2GZ4l0URUREQklI/MvNsUaSmxwX7MsYY1mCg5QYPR4QEZH/byERBgBSHdFkh1B3/GNz7aRpgSEREQkDQVtn4Otc6R2kua0bIGirFD6e6GBRnIKAiIiEh5ALAwCdg06a27oZcHmm9bwW0/D4hUTtPyAiImEkJMMAgNvr45OOm3za1Y+Bh99LsMARzdIEBxEaLCgiImEmZMPAiM4BJ/9u76bP5ZnSUDDSVrTZyPKkWJJsGigoIiLhKeTDAIDP5+PGgJPPuvtp6xuaVCgY+WyCNZK0eBtJNgsGQ6hObBQREXn4ZkQYuN2Ay0NrzwDtfUP0DrnwflU+8nfuu8ex3TK8W+LCOCsxkebpumQREZGQNuPCwO28Ph83h9x0DbnoueXC5fXh+errmAwGzEYDsZYI4qIicESatbeAiIjIXczoMCAiIiKTp6HzIiIiYU5hQEREJMwpDIiIiIQ5hQEREZEwpzAgIiIS5hQGREREwpzCgIiISJhTGBAREQlzCgMiIiJhTmFAREQkzCkMiIiIhDmFARERkTCnMCAiIhLmFAZERETCnMKAiIhImFMYEBERCXP/A66IkrAfeIYtAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -266,7 +270,7 @@ "metadata": {}, "outputs": [], "source": [ - "from qbraid.transpiler import convert_to_package" + "from qbraid.transpiler import transpile" ] }, { @@ -287,8 +291,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:\n", - "Successfully transpiled using conversions: qiskit -> qasm2 -> cirq -> stim\n" + "INFO - \n", + "Successfully transpiled using conversions: qiskit -> braket -> cirq -> stim\n" ] }, { @@ -303,7 +307,7 @@ } ], "source": [ - "stim_circuit = convert_to_package(qiskit_circuit, \"stim\", conversion_graph=graph)\n", + "stim_circuit = transpile(qiskit_circuit, \"stim\", conversion_graph=graph)\n", "\n", "type(stim_circuit)" ] @@ -354,16 +358,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[ True True]\n", + "[[False False]\n", + " [False False]\n", + " [False False]\n", " [False False]\n", - " [ True True]\n", - " [ True True]\n", " [False False]\n", " [ True True]\n", " [ True True]\n", - " [False False]\n", " [ True True]\n", - " [False False]]\n" + " [False False]\n", + " [ True True]]\n" ] } ], @@ -390,7 +394,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/qbraid_sdk/qbraid_visualization.ipynb b/qbraid_sdk/qbraid_visualization.ipynb new file mode 100644 index 0000000..10bae7f --- /dev/null +++ b/qbraid_sdk/qbraid_visualization.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3462edb1-74e9-4fd9-84b2-7580d7795230", + "metadata": {}, + "source": [ + "# qBraid-SDK: Visualization\n", + "\n", + "In this notebook, we'll go through some of the main features of the `qbraid.visualization` library." + ] + }, + { + "cell_type": "markdown", + "id": "5678ed9e-0342-49ff-89fd-6faf4d768289", + "metadata": { + "tags": [] + }, + "source": [ + "## Circuit Drawer" + ] + }, + { + "cell_type": "markdown", + "id": "8a25dc53-acf1-490d-8db0-248d5eac88a6", + "metadata": {}, + "source": [ + "First, we utilize the `circuit_drawer` function. This function takes in any type of support quantum circuit, and draws it out in the console." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "40758acf-11e1-441a-bc02-2618e3ab3116", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T : |0|1|\n", + " \n", + "q0 : -X-S-\n", + "\n", + "T : |0|1|\n" + ] + } + ], + "source": [ + "from qbraid.interface import random_circuit\n", + "from qbraid.visualization import circuit_drawer\n", + "\n", + "braket_circuit = random_circuit(\"braket\")\n", + "circuit_drawer(braket_circuit)" + ] + }, + { + "cell_type": "markdown", + "id": "a2e4337b-d797-433c-b506-c8c5acbec12c", + "metadata": {}, + "source": [ + "Let's take this circuit and transpile it to another language to see that it can still be visualized." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "10ca28b7-108b-43f3-a95c-640c1189ddd2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
        ┌───┐┌───┐ ░ ┌─┐\n",
+       "     q: ┤ X ├┤ S ├─░─┤M├\n",
+       "        └───┘└───┘ ░ └╥┘\n",
+       "meas: 1/══════════════╩═\n",
+       "                      0
" + ], + "text/plain": [ + " ┌───┐┌───┐ ░ ┌─┐\n", + " q: ┤ X ├┤ S ├─░─┤M├\n", + " └───┘└───┘ ░ └╥┘\n", + "meas: 1/══════════════╩═\n", + " 0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qbraid.transpiler import transpile\n", + "\n", + "qiskit_circuit = transpile(braket_circuit, \"qiskit\")\n", + "circuit_drawer(qiskit_circuit)" + ] + }, + { + "cell_type": "markdown", + "id": "f1c4a05b-e4f9-4b32-bbde-a97a9f668721", + "metadata": {}, + "source": [ + "Additionally, if the library you are working with has options for visualization, those options can be expressed as `kwargs` in `circuit_drawer`. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5665e703-a11f-4eda-9918-c8ef4acb540e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuit_drawer(qiskit_circuit, output=\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "id": "935cb58a-2a05-4801-bb29-3656805d502d", + "metadata": {}, + "source": [ + "## Conversion Graphs" + ] + }, + { + "cell_type": "markdown", + "id": "a9b4f5e3-f78f-4518-99b3-b0690483aa6d", + "metadata": {}, + "source": [ + "Given a `ConversionGraph`, you can use `plot_conversion_graph` to display the connections between different quantum frameworks." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "02020507-4ea0-4c02-b362-e34a1bf87bb8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from qbraid.transpiler import ConversionGraph\n", + "from qbraid.visualization import plot_conversion_graph\n", + "\n", + "graph = ConversionGraph()\n", + "plot_conversion_graph(graph, legend=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b82e5bf4-eae6-4a72-adaf-e1a8a5daca6b", + "metadata": {}, + "source": [ + "## Histograms/Distributions" + ] + }, + { + "cell_type": "markdown", + "id": "9c4d35fe-b7b0-45be-a37d-856c737025cf", + "metadata": {}, + "source": [ + "When you have a qBraid quantum job, you can get the measurement counts from it and also plot it using the qBraid-SDK." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e6536882-ecee-4238-abf2-5b5c91bb5a25", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qbraid.runtime.braket import BraketProvider\n", + "\n", + "provider = BraketProvider()\n", + "provider.get_devices()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4fd24d72-11a8-499a-af1f-d3993a5152d5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "device = provider.get_device(\"arn:aws:braket:::device/quantum-simulator/amazon/sv1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "820b1178-76a3-4883-a7a4-b9351fbd9b30", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from braket.circuits import Circuit\n", + "\n", + "circuit = Circuit().h(0).cnot(0, 1)\n", + "result = device.run(circuit, shots=1000).result()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "48fd5e7f-4f96-4d14-875d-54c58c2e241d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from qbraid.visualization import plot_histogram, plot_distribution\n", + "\n", + "plot_histogram(result.raw_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e8da20e2-0b67-48cb-85e9-a4dda1c19de5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+fS5vPhfbt2/3/GH87VwUFBS0WD6Rmpqq8vLyFmuEEhISVF9f/51zERMTo969e2vdunVyuxsfY+Hh4erXr5+2bNmiI0eOSJK6dOmiIUOGaMeOHTpw4ECLuSguLlZJSYnnmMOHD1dVVZV27tzpqQ0ePFiGYWjbtm0t5qJ79+7auHGjpxYVFaXY2Fht2rRJDQ0NkqRu3bpp4MCB+uabb1RTUyNJCgoK0rBhw7Rr1y5VVFR49h85cqRKS0tVVFTUYi7q6uqUm5vrqcXFxSk4OFhfffWVp2a329WrV68Wa2+/nYtTncj4vwz3tzPpByUlJYqOjtbnn3+uMWPGeOp33323Vq9erS+//PI7j9HQ0KDBgwdr6tSpevDBB4+7va6uzvOglaTq6mrZ7XZVVVUpJCSkfe5IM3z8AnByfPwCAF9VV1crNDS0Vb+//XrmJjw8XDabrcVfApJUXl6uqKioVh2jY8eOGj58+AnfLig1puzAwMDT7hUAAJwf/LrmJiAgQMnJycrKyvLUXC6XsrKyWpzJORWn06ktW7aoV69eZ6pNAABwHvH7p4JnZGRo+vTpSklJ0ciRI7VgwQLV1tZ63q43bdo0RUdHKzMzU5L0hz/8QaNHj9aAAQN08OBBPfroo9q9e7duu+02f94NAABwjvB7uElLS1NFRYXmzJmjsrIyJSUlaeXKlZ5FxoWFhTJN7wmmAwcOaObMmSorK1NYWJiSk5P1+eefa8iQIf66CwAA4Bzi1wXF/tCWBUm+YEExcHIsKAbgq7b8/vb7RfwAAADaE+EGAABYCuEGAABYit8XFAPA+Ya1dcCp+Xt9HWduAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApZwT4eaZZ56Rw+FQUFCQRo0apbVr17Zqv7feekuGYej6668/sw0CAIDzht/DzeLFi5WRkaG5c+dq48aNSkxM1IQJE7R3795T7ldQUKBf//rXuvjii89SpwAA4Hzg93DzxBNPaObMmUpPT9eQIUO0cOFCderUSS+//PJJ93E6nbr55ps1b9489evX7yx2CwAAznXtEm6qq6u1bNkyffPNN23ar76+Xhs2bND48eO9DZmmxo8frzVr1px0vz/84Q+KiIjQT3/60+8co66uTtXV1S2+AACAdXXwZacpU6bokksu0axZs3TkyBGlpKSooKBAbrdbb731liZPntyq41RWVsrpdCoyMrJFPTIyUtu3bz/hPp9++qn++te/Kjs7u1VjZGZmat68ecfV169fry5dukiSkpKSVFNTo/z8fM/t8fHxstls2rp1q6fmcDjUo0cPbdiwoUWvffr0UXZ2turr6/WTgU4V1xr6YI+pq2Kc6t2pcbuaBuntXTaNiXBpcDe3Z/9Xck0N7ubW6AhvbWmBqc4dpAkxLk8tq8TUvqPSlH7e2oZKQ5v3m5oR55RpNNZ2VBv6pMzUDX2cCgtsrO09Kr1XaNPlvV1ydGkcp84lvZlnU3K4S4ndvWMvyjcV3VkaF+UdZ3mRKZdbujbWW/u03NCuGkO3DvDWthwwtK7C1NT+TgXbGmuFhwx9VGLqGrtTUcGNtaoGackumy6KdGlQqHfsl3NtGhrm0sie3tqSAlNdO0o/iPaO89EeUwfrpR/19dbWVRjacsBU+kCnmqZCuVWGPi03daPDqW4BjbXyI9LyIpuu6O1Sn6a5OOqUFuXblBLu0rBmc/Fmnil7F7cuifLW3its/Hvgh83m4r9lhooOGbq52Vx8td/Q+kpTN/V3KqhpLnYfMpRVYmqi3anIprk4WC8tLbBpbKRLA5vmwi3plVybEsJcSm02F+/sMtUtQBrfbC4+2GOqpkGa7PDW1lYY+vqAqZ8MdHpqOVWGPis3NbmvU6EdG2tlR6QVRTaN7+1SbNNcHHFKf8+3KbWnSwlh3rHfyDPVt6tbYyO9tX8VmjINaaLdO/bqMlN7aqWb+ntrm/cb2lBp6uYBTs96urCwMMXFxWnbtm06dOiQJCk4OFgJCQnauXOnKisrJUmGYSg1NVUlJSUqLi72HDMxMVG1tbXKy8vz1AYNGqSAgABt2bLFU4uNjVVkZKTWrVvnqUVERMjhcGjz5s2qq6uTJIWEhCg+Pl45OTmqqqqSJAUEBCgpKUm7d+9WeXm5Z//k5GQNCnXpomZz8e5uUwGmdHWzuVhVaqrsiPTjZo/ZTfsMbdpn6tYBTnVs+tNyZ42hVaWmJsU6FR7UWNtXJ72726ZxUS71D2kc55hben2HTUndXRoR7h178U5TPYOky3t7x1lZbOqoU7q+j7e2Zq+hnCpDM+K8tW0HDX2x19SUvk51afp/seewoX8Xm5oQ41J0p8ZxDjVI/9hl0+gIl4Y0e/56dYepQaFujWn2/LVst6kgm3RVs+evj0tMVRyV0prNxcZKQ9n7TU2Lc6pD04M2v9rQ6jJT1/VxqkfT81flUemfhTZd2sulfl0bx2lwSW/k2TS8h0vDe3jHfmunqahg6dJe3nHeLzJV75KuazYXn5Ubyq82NK3ZXGw9YOjLClNp/Zzq3PSbkedy35/LS0tLVVRU5KklJCSorq5Oubm5nlpcXJyCg4P11VdfeWp2u129evVqsfY2PDxc/fr1a/H7+LsYbrfb/d2btRQVFaV///vfSkxM1KJFizR37lxt3rxZr732ml544QVt2rSpVccpKSlRdHS0Pv/8c40ZM8ZTv/vuu7V69Wp9+eWXLbavqanRsGHD9Oyzz+rqq6+WJM2YMUMHDx7UsmXLTjhGXV2d5wlMajzLZLfbVVVVpZCQkDbe8+/muGd5ux8TsIqC+RP93UK74HEOnNqZeKxXV1crNDS0Vb+/fTpzU1VVpe7du0uSVq5cqcmTJ6tTp06aOHGifvOb37T6OOHh4bLZbC3+KpKk8vJyRUVFHbd9fn6+CgoKdO2113pqLldj2uzQoYNycnLUv3//FvsEBgYqMDCw1T0BAIDzm09rbux2u9asWaPa2lqtXLlSP/jBDyRJBw4cUFBQUKuPExAQoOTkZGVlZXlqLpdLWVlZLc7kfCs+Pl5btmxRdna252vSpEm67LLLlJ2dLbvd7svdAQAAFuLTmZvZs2fr5ptvVpcuXRQbG6tLL71UkvTf//5XCQkJbTpWRkaGpk+frpSUFI0cOVILFixQbW2t0tPTJUnTpk1TdHS0MjMzFRQUpKFDh7bYv1u3bpJ0XB0AAHw/+RRu7rjjDo0cOVJFRUW68sorZZqNJ4D69eunhx56qE3HSktLU0VFhebMmaOysjIlJSVp5cqVnkXGhYWFnuMDAAB8F58WFH+rvr5eu3btUv/+/dWhg0856axry4IkX7DQEDg5FhQD3w/+XlDs0ymRw4cP66c//ak6deqkCy64QIWFhZKkO++8U/Pnz/flkAAAAO3Cp3Bz7733avPmzVq1alWLBcTjx4/X4sWL2605AACAtvLptaRly5Zp8eLFGj16tAzD8NQvuOCCFhfCAwAAONt8OnNTUVGhiIiI4+q1tbUtwg4AAMDZ5lO4SUlJ0fLl3gV13waal1566YTXpwEAADhbfHpZ6o9//KOuvvpqbdu2TceOHdNTTz2lbdu26fPPP9fq1avbu0cAAIBW8+nMzdixY5Wdna1jx44pISFBH3zwgSIiIrRmzRolJye3d48AAACt5vPFafr3768XX3yxPXsBAAA4ba0ON9XV1Z6L5lRXV59y2zNxcTwAAIDWaHW4CQsLU2lpqSIiItStW7cTvivK7XbLMAw5nc52bRIAAKC1Wh1uPv74Y3Xv3l2S9J///OeMNQQAAHA6Wh1uxo0b5/m+b9++stvtx529cbvdKioqar/uAAAA2sind0v17dtXFRUVx9X379+vvn37nnZTAAAAvvIp3Hy7tub/OnToUIvPmgIAADjb2vRW8IyMDEmNVyS+//771alTJ89tTqdTX375pZKSktq1QQAAgLZoU7jZtGmTpMYzN1u2bFFAQIDntoCAACUmJurXv/51+3YIAADQBm0KN9++Syo9PV1PPfUU17MBAADnHJ+uUPzKK6+0dx8AAADtotXh5sYbb9Srr76qkJAQ3XjjjafcdunSpafdGAAAgC9aHW5CQ0M975AKDQ09Yw0BAACcjlaHm+YvRfGyFAAAOFf5dJ0bAACAc1Wrz9wMHz78hBfuO5GNGzf63BAAAMDpaHW4uf76689gGwAAAO2j1eFm7ty5Z7IPAACAdsGaGwAAYCmtPnPTvXt35ebmKjw8XGFhYadcf7N///52aQ4AAKCtWh1unnzySXXt2lWStGDBgjPVDwAAwGlpdbiZPn36Cb8HAAA4l/j02VKS5HQ69b//+7/65ptvJElDhgzRddddpw4dfD4kAADAafMpiWzdulWTJk1SWVmZBg0aJEl65JFH1LNnT/3rX//S0KFD27VJAACA1vLp3VK33XabLrjgAhUXF2vjxo3auHGjioqKNGzYMP3sZz9r7x4BAABazaczN9nZ2Vq/fr3CwsI8tbCwMD388MNKTU1tt+YAAADayqczNwMHDlR5eflx9b1792rAgAGn3RQAAICvWh1uqqurPV+ZmZn6f//v/+mdd95RcXGxiouL9c4772j27Nl65JFHzmS/AAAAp9Tql6W6devW4sJ9brdbU6ZM8dTcbrck6dprr5XT6WznNgEAAFqn1eHmP//5z5nsAwAAoF20OtyMGzfuTPYBAADQLk7rinuHDx9WYWGh6uvrW9SHDRt2Wk0BAAD4yqdwU1FRofT0dL3//vsnvJ01NwAAwF98eiv47NmzdfDgQX355ZcKDg7WypUr9dprrykuLk7//Oc/27tHAACAVvPpzM3HH3+sd999VykpKTJNU3369NGVV16pkJAQZWZmauLEie3dJwAAQKv4dOamtrZWERERkhqvTFxRUSFJSkhI0MaNG9uvOwAAgDbyKdwMGjRIOTk5kqTExEQ9//zz2rNnjxYuXKhevXq1a4MAAABt4dPLUnfddZdKS0slSXPnztVVV12lN998UwEBAXr11Vfbsz8AAIA28Snc3HLLLZ7vk5OTtXv3bm3fvl2xsbEKDw9vt+YAAADa6rSucyM1fuxCcHCwRowY0R79AAAAnBaf1txI0l//+lcNHTpUQUFBCgoK0tChQ/XSSy+1Z28AAABt5tOZmzlz5uiJJ57QnXfeqTFjxkiS1qxZo1/+8pcqLCzUH/7wh3ZtEgAAoLV8CjfPPfecXnzxRU2dOtVTmzRpkoYNG6Y777yTcAMAAPzGp5elGhoalJKSclw9OTlZx44dO+2mAAAAfOVTuLn11lv13HPPHVd/4YUXdPPNN7f5eM8884wcDoeCgoI0atQorV279qTbLl26VCkpKerWrZs6d+6spKQkvfHGG20eEwAAWFOrX5bKyMjwfG8Yhl566SV98MEHGj16tCTpyy+/VGFhoaZNm9amBhYvXqyMjAwtXLhQo0aN0oIFCzRhwgTl5OR4roLcXPfu3XXfffcpPj5eAQEBeu+995Senq6IiAhNmDChTWMDAADrMdxut7s1G1522WWtO6Bh6OOPP251A6NGjVJqaqqefvppSZLL5ZLdbtedd96pe+65p1XHGDFihCZOnKgHH3zwO7etrq5WaGioqqqqFBIS0uo+W8txz/J2PyZgFQXzrfG5czzOgVM7E4/1tvz+bvWZm//85z+n3dj/VV9frw0bNujee+/11EzT1Pjx47VmzZrv3N/tduvjjz9WTk6OHnnkkRNuU1dXp7q6Os/P1dXVp984AAA4Z532RfyKi4slSTExMW3et7KyUk6nU5GRkS3qkZGR2r59+0n3q6qqUnR0tOrq6mSz2fTss8/qyiuvPOG2mZmZmjdv3nH19evXq0uXLpKkpKQk1dTUKD8/33N7fHy8bDabtm7d6qk5HA716NFDGzZsaNFrnz59lJ2drfr6ev1koFPFtYY+2GPqqhinendq3K6mQXp7l01jIlwa3M17suyVXFODu7k1OsJbW1pgqnMHaUKMy1PLKjG176g0pZ+3tqHS0Ob9pmbEOWUajbUd1YY+KTN1Qx+nwgIba3uPSu8V2nR5b5ccXRrHqXNJb+bZlBzuUmJ379iL8k1Fd5bGRXnHWV5kyuWWro311j4tN7SrxtCtA7y1LQcMraswNbW/U8G2xlrhIUMflZi6xu5UVHBjrapBWrLLposiXRoU6h375Vybhoa5NLKnt7akwFTXjtIPor3jfLTH1MF66Ud9vbV1FYa2HDCVPtCppqlQbpWhT8tN3ehwqltAY638iLS8yKYrervUp2kujjqlRfk2pYS7NKzZXLyZZ8rexa1Lory19wobl6n9sNlc/LfMUNEhQzc3m4uv9htaX2nqpv5OBTXNxe5DhrJKTE20OxXZNBcH66WlBTaNjXRpYNNcuCW9kmtTQphLqc3m4p1dproFSOObzcUHe0zVNEiTHd7a2gpDXx8w9ZOBTk8tp8rQZ+WmJvd1KrRjY63siLSiyKbxvV2KbZqLI07p7/k2pfZ0KSHMO/Ybeab6dnVrbKS39q9CU6YhTbR7x15dZmpPrXRTf29t835DGypN3TzA6VlPFxYWpri4OG3btk2HDh2SJAUHByshIUE7d+5UZWWlpMYzwampqSopKfE810iNn2lXW1urvLw8T23QoEEKCAjQli1bPLXY2FhFRkZq3bp1nlpERIQcDoc2b97s+cMnJCRE8fHxysnJUVVVlSQpICBASUlJ2r17t8rLyz37Jycna1CoSxc1m4t3d5sKMKWrm83FqlJTZUekHzd7zG7aZ2jTPlO3DnCqY9OKx501hlaVmpoU61R4UGNtX5307m6bxkW51D+kcZxjbun1HTYldXdpRLh37MU7TfUMki7v7R1nZbGpo07p+j7e2pq9hnKqDM2I89a2HTT0xV5TU/o61aXp/8Wew4b+XWxqQoxL0Z0axznUIP1jl02jI1wa0uz569UdpgaFujWm2fPXst2mgmzSVc2evz4uMVVxVEprNhcbKw1l7zc1Lc6pDk0P2vxqQ6vLTF3Xx6keTc9flUelfxbadGkvl/p1bRynwSW9kWfT8B4uDe/hHfutnaaigqVLe3nHeb/IVL1Luq7ZXHxWbii/2tC0ZnOx9YChLytMpfVzqnPTb0aey31/Li8tLVVRUZGnlpCQoLq6OuXm5npqcXFxCg4O1ldffeWp2e129erVq8Xa2/DwcPXr16/F7+Pv0uqXpZpzuVx66KGH9Pjjj3uemLp27apf/epXuu+++2SarVunXFJSoujoaH3++eee6+VI0t13363Vq1fryy+/POn4O3fu1KFDh5SVlaUHH3xQy5Yt06WXXnrctic6c2O323lZCvADXpYCvh/Om5elmrvvvvv017/+VfPnz9dFF10kSfr000/1wAMP6OjRo3r44YdbdZzw8HDZbLYWfxVJUnl5uaKiok66n2maGjBggKTGsy7ffPONMjMzTxhuAgMDFRgY2Mp7BgAAznc+vRX8tdde00svvaTbb79dw4YN07Bhw3THHXfoxRdfbNOnggcEBCg5OVlZWVmemsvlUlZWVoszOd/F5XK1ODsDAAC+v3w6c7N//37Fx8cfV4+Pj9f+/fvbdKyMjAxNnz5dKSkpGjlypBYsWKDa2lqlp6dLkqZNm6bo6GhlZmZKalxDk5KSov79+6uurk4rVqzQG2+8ccLr7gAAgO8fn8JNYmKinn76af35z39uUX/66aeVmJjYpmOlpaWpoqJCc+bMUVlZmZKSkrRy5UrPIuPCwsIWa3hqa2t1xx13qLi4WMHBwYqPj9ff/vY3paWl+XJXAACAxfi0oHj16tWaOHGiYmNjW3xwZlFRkVasWKGLL7643RttL1znBvAfFhQD3w/+XlDs05qbcePGKTc3VzfccIMOHjyogwcP6sYbb1ROTs45HWwAAID1tfllqYaGBl111VVauHBhq98VBQAAcLa0+cxNx44dW1xwBwAA4Fzi08tSt9xyi/7617+2dy8AAACnzad3Sx07dkwvv/yyPvroIyUnJ6tz584tbn/iiSfapTkAAIC28incfP311xoxYoQktficCKnxs2AAAAD8xadwcyY+IRwAAKA9+LTmprmioqIWn/wJAADgTz6Fm2PHjun+++9XaGioHA6HHA6HQkND9fvf/14NDQ3t3SMAAECr+fSy1J133qmlS5fqT3/6U4srFD/wwAPat28fn/MEAAD8xqdws2jRIr311lu6+uqrPbVhw4bJbrdr6tSphBsAAOA3Pr0sFRgYKIfDcVy9b9++CggION2eAAAAfOZTuJk1a5YefPBB1dXVeWp1dXV6+OGHNWvWrHZrDgAAoK18ellq06ZNysrKUkxMjBITEyVJmzdvVn19va644grdeOONnm2XLl3aPp0CAAC0gk/hplu3bpo8eXKLmt1ub5eGAAAATodP4eaVV15p7z4AAADaxWlfxG/+/Pk6ePBgO7QCAABw+k473Pzxj3/U/v3726MXAACA03ba4cbtdrdHHwAAAO3itMMNAADAucSnBcXNbdu2Tb17926PXgAAAE7baYcb3gIOAADOJa0ON927d1dubq7Cw8MVFhYmwzBOui0LjAEAgL+0Otw8+eST6tq1q+f7U4UbAAAAf2l1uJk+fbrn+xkzZpyJXgAAAE6bT++W2rhxo7Zs2eL5+d1339X111+v3/3ud6qvr2+35gAAANrKp3Dz85//XLm5uZKknTt3Ki0tTZ06ddLbb7+tu+++u10bBAAAaAufwk1ubq6SkpIkSW+//bbGjRunRYsW6dVXX9WSJUvasz8AAIA28SncuN1uuVwuSdJHH32ka665RlLj28IrKyvbrzsAAIA28incpKSk6KGHHtIbb7yh1atXa+LEiZKkXbt2KTIysl0bBAAAaAufws2CBQu0ceNGzZo1S/fdd58GDBggSXrnnXd04YUXtmuDAAAAbeHTFYqHDRvW4t1S33r00Udls9lOuykAAABfnfbHLzQXFBTUnocDAABoM5/CjdPp1JNPPql//OMfKiwsPO7aNnz8AgAA8Bef1tzMmzdPTzzxhNLS0lRVVaWMjAzdeOONMk1TDzzwQDu3CAAA0Ho+hZs333xTL774on71q1+pQ4cOmjp1ql566SXNmTNHX3zxRXv3CAAA0Go+hZuysjIlJCRIkrp06aKqqipJ0g9/+EMtX768/boDAABoI5/CTUxMjEpLSyVJ/fv31wcffCBJWrdunQIDA9uvOwAAgDbyKdzccMMNysrKkiTdeeeduv/++xUXF6dp06bpJz/5Sbs2CAAA0BY+vVtq/vz5nu/T0tIUGxurNWvWKC4uTtdee227NQcAANBW7XKdmzFjxmjMmDHtcSgAAIDT4lO4ef311095+7Rp03xqBgAA4HT5FG7uuuuuFj83NDTo8OHDCggIUKdOnQg3AADAb3xaUHzgwIEWX4cOHVJOTo7Gjh2rv//97+3dIwAAQKv5FG5OJC4uTvPnzz/urA4AAMDZ1G7hRpI6dOigkpKS9jwkAABAm/i05uaf//xni5/dbrdKS0v19NNP66KLLmqXxgAAAHzhU7i5/vrrW/xsGIZ69uypyy+/XI8//nh79AUAAOATn8KNy+WSJFVUVCggIEChoaHt2hQAAICv2rzm5uDBg/rFL36h8PBwRUVFqXv37oqKitK9996rw4cPn4keAQAAWq1N4Wb//v0aNWqUXnvtNU2ePFmPP/64Hn/8cU2aNEl/+ctfdMkll+jo0aNau3at/vznP7f6uM8884wcDoeCgoI0atQorV279qTbvvjii7r44osVFhamsLAwjR8//pTbAwCA75c2hZs//OEPCggIUH5+vp5//nnNnj1bs2fP1gsvvKC8vDzV19fr1ltv1ZVXXtnql6oWL16sjIwMzZ07Vxs3blRiYqImTJigvXv3nnD7VatWaerUqfrPf/6jNWvWyG636wc/+IH27NnTlrsCAAAsqk3hZtmyZXrssccUGRl53G1RUVH605/+pCVLligjI0PTp09v1TGfeOIJzZw5U+np6RoyZIgWLlyoTp066eWXXz7h9m+++abuuOMOJSUlKT4+Xi+99JJcLpfnU8oBAMD3W5vCTWlpqS644IKT3j506FCZpqm5c+e26nj19fXasGGDxo8f723INDV+/HitWbOmVcc4fPiwGhoa1L1791ZtDwAArK1N4SY8PFwFBQUnvX3Xrl2KiIho9fEqKyvldDqPOxMUGRmpsrKyVh3jt7/9rXr37t0iIDVXV1en6urqFl8AAMC62vRW8AkTJui+++7Thx9+qICAgBa31dXV6f7779dVV13Vrg2eyvz58/XWW29p1apVCgoKOuE2mZmZmjdv3nH19evXq0uXLpKkpKQk1dTUKD8/33N7fHy8bDabtm7d6qk5HA716NFDGzZs8NQiIyPVp08fZWdnq76+Xj8Z6FRxraEP9pi6Ksap3p0at6tpkN7eZdOYCJcGd3N79n8l19Tgbm6NjvDWlhaY6txBmhDj8tSySkztOypN6eetbag0tHm/qRlxTplGY21HtaFPykzd0MepsMDG2t6j0nuFNl3e2yVHl8Zx6lzSm3k2JYe7lNjdO/aifFPRnaVxUd5xlheZcrmla2O9tU/LDe2qMXTrAG9tywFD6ypMTe3vVLCtsVZ4yNBHJaausTsVFdxYq2qQluyy6aJIlwaFesd+OdemoWEujezprS0pMNW1o/SDaO84H+0xdbBe+lFfb21dhaEtB0ylD3SqaSqUW2Xo03JTNzqc6tb037X8iLS8yKYrervUp2kujjqlRfk2pYS7NKzZXLyZZ8rexa1Lory19wob/x74YbO5+G+ZoaJDhm5uNhdf7Te0vtLUTf2dCmqai92HDGWVmJpodyqyaS4O1ktLC2waG+nSwKa5cEt6JdemhDCXUpvNxTu7THULkMY3m4sP9piqaZAmO7y1tRWGvj5g6icDnZ5aTpWhz8pNTe7rVGjHxlrZEWlFkU3je7sU2zQXR5zS3/NtSu3pUkKYd+w38kz17erW2Ehv7V+FpkxDmmj3jr26zNSeWumm/t7a5v2GNlSaunmA07P4PywsTHFxcdq2bZsOHTokSQoODlZCQoJ27typyspKSY3X0EpNTVVJSYmKi4s9x0xMTFRtba3y8vI8tUGDBikgIEBbtmzx1GJjYxUZGal169Z5ahEREXI4HNq8ebPq6uokSSEhIYqPj1dOTo6qqqokSQEBAUpKStLu3btVXl7u2T85OVmDQl26qNlcvLvbVIApXd1sLlaVmio7Iv242WN20z5Dm/aZunWAUx2b/rTcWWNoVampSbFOhTc9je2rk97dbdO4KJf6hzSOc8wtvb7DpqTuLo0I9469eKepnkHS5b2946wsNnXUKV3fx1tbs9dQTpWhGXHe2raDhr7Ya2pKX6e6NP2/2HPY0L+LTU2IcSm6U+M4hxqkf+yyaXSES0OaPX+9usPUoFC3xjR7/lq221SQTbqq2fPXxyWmKo5Kac3mYmOloez9pqbFOdWh6UGbX21odZmp6/o41aPp+avyqPTPQpsu7eVSv66N4zS4pDfybBrew6XhPbxjv7XTVFSwdGkv7zjvF5mqd0nXNZuLz8oN5VcbmtZsLrYeMPRlham0fk51bvrNyHO578/lpaWlKioq8tQSEhJUV1en3NxcTy0uLk7BwcH66quvPDW73a5evXq1eKNQeHi4+vXr1+L38Xcx3G63+7s3a1RcXKyUlBQFBgbqF7/4heLj4+V2u/XNN9/o2WefVV1dndatW6fY2NhWHa++vl6dOnXSO++80+LCgNOnT9fBgwf17rvvnnTfxx57TA899JA++ugjpaSknHS7uro6zxOYJFVXV8tut6uqqkohISGt6rMtHPcsb/djAlZRMH+iv1toFzzOgVM7E4/16upqhYaGtur3d5vO3MTExGjNmjW64447dO+99+rbXGQYhq688ko9/fTTrQ42UuNfRsnJycrKyvKEm28XB8+aNeuk+/3pT3/Sww8/rH//+9+nDDaSFBgYqMDAwFb3BAAAzm9tvkJx37599f777+vAgQPasWOHJGnAgAE+L+j99p1VKSkpGjlypBYsWKDa2lqlp6dLkqZNm6bo6GhlZmZKkh555BHNmTNHixYtksPh8KzN6dKli+dlJgAA8P3l08cvSI2vmY8cOfK0G0hLS1NFRYXmzJmjsrIyJSUlaeXKlZ5FxoWFhTJN77rn5557TvX19frRj37U4jhz587VAw88cNr9AACA85vP4aY9zZo166QvQ61atarFz6d6txYAAECbP1sKAADgXEa4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAluL3cPPMM8/I4XAoKChIo0aN0tq1a0+67datWzV58mQ5HA4ZhqEFCxacvUYBAMB5wa/hZvHixcrIyNDcuXO1ceNGJSYmasKECdq7d+8Jtz98+LD69eun+fPnKyoq6ix3CwAAzgd+DTdPPPGEZs6cqfT0dA0ZMkQLFy5Up06d9PLLL59w+9TUVD366KP68Y9/rMDAwLPcLQAAOB/4LdzU19drw4YNGj9+vLcZ09T48eO1Zs2adhunrq5O1dXVLb4AAIB1dfDXwJWVlXI6nYqMjGxRj4yM1Pbt29ttnMzMTM2bN++4+vr169WlSxdJUlJSkmpqapSfn++5PT4+XjabTVu3bvXUHA6HevTooQ0bNrTot0+fPsrOzlZ9fb1+MtCp4lpDH+wxdVWMU707NW5X0yC9vcumMREuDe7m9uz/Sq6pwd3cGh3hrS0tMNW5gzQhxuWpZZWY2ndUmtLPW9tQaWjzflMz4pwyjcbajmpDn5SZuqGPU2FNJ7f2HpXeK7Tp8t4uObo0jlPnkt7Msyk53KXE7t6xF+Wbiu4sjYvyjrO8yJTLLV0b6619Wm5oV42hWwd4a1sOGFpXYWpqf6eCbY21wkOGPioxdY3dqajgxlpVg7Rkl00XRbo0KNQ79su5Ng0Nc2lkT29tSYGprh2lH0R7x/loj6mD9dKP+npr6yoMbTlgKn2gU01TodwqQ5+Wm7rR4VS3gMZa+RFpeZFNV/R2qU/TXBx1SovybUoJd2lYs7l4M8+UvYtbl0R5a+8VNv498MNmc/HfMkNFhwzd3GwuvtpvaH2lqZv6OxXUNBe7DxnKKjE10e5UZNNcHKyXlhbYNDbSpYFNc+GW9EquTQlhLqU2m4t3dpnqFiCNbzYXH+wxVdMgTXZ4a2srDH19wNRPBjo9tZwqQ5+Vm5rc16nQjo21siPSiiKbxvd2KbZpLo44pb/n25Ta06WEMO/Yb+SZ6tvVrbGR3tq/Ck2ZhjTR7h17dZmpPbXSTf29tc37DW2oNHXzAKdnTV1YWJji4uK0bds2HTp0SJIUHByshIQE7dy5U5WVlZIkwzCUmpqqkpISFRcXe46ZmJio2tpa5eXleWqDBg1SQECAtmzZ4qnFxsYqMjJS69at89QiIiLkcDi0efNm1dXVSZJCQkIUHx+vnJwcVVVVSZICAgKUlJSk3bt3q7y83LN/cnKyBoW6dFGzuXh3t6kAU7q62VysKjVVdkT6cbPH7KZ9hjbtM3XrAKc6Nv1pubPG0KpSU5NinQoPaqztq5Pe3W3TuCiX+oc0jnPMLb2+w6ak7i6NCPeOvXinqZ5B0uW9veOsLDZ11Cld38dbW7PXUE6VoRlx3tq2g4a+2GtqSl+nujT9v9hz2NC/i01NiHEpulPjOIcapH/ssml0hEtDmj1/vbrD1KBQt8Y0e/5atttUkE26qtnz18clpiqOSmnN5mJjpaHs/aamxTnVoelBm19taHWZqev6ONWj6fmr8qj0z0KbLu3lUr+ujeM0uKQ38mwa3sOl4T28Y7+101RUsHRpL+847xeZqndJ1zWbi8/KDeVXG5rWbC62HjD0ZYWptH5OdW76zchzue/P5aWlpSoqKvLUEhISVFdXp9zcXE8tLi5OwcHB+uqrrzw1u92uXr16tVh/Gx4ern79+rX4ffxdDLfb7f7uzdpfSUmJoqOj9fnnn2vMmDGe+t13363Vq1fryy+/POX+DodDs2fP1uzZs0+5XV1dnecJTJKqq6tlt9tVVVWlkJCQ07oPJ+zrnuXtfkzAKgrmT/R3C+2CxzlwamfisV5dXa3Q0NBW/f7225mb8PBw2Wy2Fn8RSVJ5eXm7LhYODAxkfQ4AAN8jfltzExAQoOTkZGVlZXlqLpdLWVlZLc7kAAAAtIXfztxIUkZGhqZPn66UlBSNHDlSCxYsUG1trdLT0yVJ06ZNU3R0tDIzMyU1LkLetm2b5/s9e/YoOztbXbp00YABA/x2PwAAwLnDr+EmLS1NFRUVmjNnjsrKypSUlKSVK1d6FhkXFhbKNL0nl0pKSjR8+HDPz4899pgee+wxjRs3TqtWrTrb7QMAgHOQX8ONJM2aNUuzZs064W3/N7A4HA75af0zAAA4T/j94xcAAADaE+EGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYyjkRbp555hk5HA4FBQVp1KhRWrt27Sm3f/vttxUfH6+goCAlJCRoxYoVZ6lTAABwrvN7uFm8eLEyMjI0d+5cbdy4UYmJiZowYYL27t17wu0///xzTZ06VT/96U+1adMmXX/99br++uv19ddfn+XOAQDAucjv4eaJJ57QzJkzlZ6eriFDhmjhwoXq1KmTXn755RNu/9RTT+mqq67Sb37zGw0ePFgPPvigRowYoaeffvosdw4AAM5FHfw5eH19vTZs2KB7773XUzNNU+PHj9eaNWtOuM+aNWuUkZHRojZhwgQtW7bshNvX1dWprq7O83NVVZUkqbq6+jS7PzFX3eEzclzACs7U4+5s43EOnNqZeKx/e0y32/2d2/o13FRWVsrpdCoyMrJFPTIyUtu3bz/hPmVlZSfcvqys7ITbZ2Zmat68ecfV7Xa7j10D8FXoAn93AOBsOJOP9ZqaGoWGhp5yG7+Gm7Ph3nvvbXGmx+Vyaf/+/erRo4cMw/BjZzjTqqurZbfbVVRUpJCQEH+3A+AM4bH+/eB2u1VTU6PevXt/57Z+DTfh4eGy2WwqLy9vUS8vL1dUVNQJ94mKimrT9oGBgQoMDGxR69atm+9N47wTEhLCEx7wPcBj3fq+64zNt/y6oDggIEDJycnKysry1Fwul7KysjRmzJgT7jNmzJgW20vShx9+eNLtAQDA94vfX5bKyMjQ9OnTlZKSopEjR2rBggWqra1Venq6JGnatGmKjo5WZmamJOmuu+7SuHHj9Pjjj2vixIl66623tH79er3wwgv+vBsAAOAc4fdwk5aWpoqKCs2ZM0dlZWVKSkrSypUrPYuGCwsLZZreE0wXXnihFi1apN///vf63e9+p7i4OC1btkxDhw71113AOSowMFBz58497mVJANbCYx3/l+FuzXuqAAAAzhN+v4gfAABAeyLcAAAASyHcAAAASyHcAAAASyHcAAAASyHc4HuDNwYCwPeD369zA5wppaWlKioq0oEDBzR+/HjZbDZ/twQAOAsIN7Ckr776SpMmTVJgYKDKy8vVq1cvzZkzRxMmTFD37t393R6AdrJ3714FBATwmYFogZelYDkVFRVKS0vTzTffrPfff1/btm1TYmKiHnzwQf35z39WRUWFv1sE0A6++eYb2e12zZw5U9XV1f5uB+cQwg0sp6KiQkePHtWNN96ofv36qXfv3nrrrbc0adIkLV26VK+++qoOHz7s7zYBnIby8nLddtttGjt2rFatWqXbbruNgAMPwg0sp76+Xg0NDZ4Ac+TIEUnS/Pnzddlll+m5555TXl6eJBYZA+erTZs2yeFw6JFHHtHy5cuVlZVFwIEHny0FS3C5XHK73Z5FwxdffLFM09Tq1aslSXV1dZ4P1UtNTdWAAQP097//3W/9Ajg9FRUV2rp1qy699FJJ0hdffKGJEyfqiiuu0IsvvqjQ0FBJjX/AGIbhx07hD5y5wXlv27ZtmjZtmiZMmKCZM2dq9erVeuqpp7Rnzx5NmTJFUuOnBh87dkySdMkll6i2ttafLQPwgdPp9Hzfs2dPT7BxuVwaPXq0VqxYoaysLM8anIaGBi1cuFAffvihnzqGvxBucF7LycnRhRdeKKfTqdTUVK1bt06/+c1v9NJLL+nBBx/Uhg0bdMMNN6ihoUGm2fjffe/evercubOOHTvGy1LAeSI3N1cLFixQaWnpcbd9+9geNWqU3n//fU/A+fnPf6677rpL/fr1O9vtws94WQrnLbfbrd///vfKy8vT4sWLJUk1NTVasGCB3nvvPQ0YMEBTpkzR3XffLUkaMmSIAgICtHz5cn3xxRcaOnSoP9sH0Ep5eXkaNWqUDhw4oHvuuUcZGRkKDw8/6fafffaZLr74YoWFhenDDz/UiBEjzmK3OBdwnRuctwzDUElJicrKyjy1rl27avbs2QoODtbSpUuVm5ur9evX6+GHH9a+ffsUFBSktWvXasiQIX7sHEBr1dbWKjMzU5MmTVJqaqpmzZqlY8eO6e677z5hwKmvr9ff/vY3denSRZ988gmP9e8pwg3OS98uEhwxYoR27NihnJwcDRo0SFJjwPnpT3+qnJwcLVmyRL/+9a81f/58SY2vzX97ChvAuc80TSUnJ6tHjx5KS0tTeHi4fvzjH0vSCQPO5s2b9cknnygrK4tg8z3Gy1I4r+Xn52v06NGaNGmSnnrqKXXp0sUTfIqKitSnTx+99957uuaaayTxzgngfFRbW6vOnTt7fl68eLGmTp2qX/3qV7rnnnvUo0cPuVwu7dmzR3a7XQcOHFBYWJgfO4a/ceYG57X+/fvrH//4h66++moFBwfrgQce8Pwl17FjRw0bNqzFkxzBBjj/fBtsnE6nTNNUWlqa3G63brrpJhmGodmzZ+uxxx7Trl27tGjRIoINCDc4/1122WV6++239T//8z8qLS3VlClTNGzYML3++uvau3ev7Ha7v1sE0A5sNpvcbrdcLpd+/OMfyzAM3XrrrfrnP/+p/Px8rV27VsHBwf5uE+cAXpaCZWzcuFEZGRkqKChQhw4dZLPZ9NZbb2n48OH+bg1AO/r215ZhGLriiiuUnZ2tVatWKSEhwc+d4VxBuIGlVFdXa//+/aqpqVGvXr1O+XZRAOcvp9Op3/zmN1qwYIGys7M1bNgwf7eEcwgvS8FSQkJCFBIS4u82AJwFF1xwgTZu3EiwwXE4cwMAOC/x7kecDBf8AACclwg2OBnCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsJT/D8Qo8KMQyCRZAAAAAElFTkSuQmCC", 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