Hello update (tensorflow#156)

* Modified Hello Many worlds tutorial to verify outputs. * metadata fix. * last of metadata. * Billy feedback. * break things down. * removed the. * Update docs/tutorials/hello_many_worlds.ipynb Co-Authored-By: Billy Lamberta <[email protected]> * Removed blank cell at end. Co-authored-by: Michael Broughton <[email protected]> Co-authored-by: Billy Lamberta <[email protected]>
ahrPope · Mar 11, 2020 · c40af6b · c40af6b
1 parent b75e063
commit c40af6b
Showing 1 changed file with 85 additions and 6 deletions.
diff --git a/docs/tutorials/hello_many_worlds.ipynb b/docs/tutorials/hello_many_worlds.ipynb
@@ -1,4 +1,6 @@
 {
+  "nbformat": 4,
+  "nbformat_minor": 0,
   "metadata": {
     "colab": {
       "name": "hello_many_worlds.ipynb",
@@ -25,8 +27,6 @@
       "version": "3.7.5rc1"
     }
   },
-  "nbformat": 4,
-  "nbformat_minor": 0,
   "cells": [
     {
       "cell_type": "markdown",
@@ -793,11 +793,13 @@
       },
       "source": [
         "random_rotations = np.random.uniform(0, 2 * np.pi, 3)\n",
+        "noisy_preparation = cirq.Circuit(\n",
+        "  cirq.rx(random_rotations[0])(qubit),\n",
+        "  cirq.ry(random_rotations[1])(qubit),\n",
+        "  cirq.rz(random_rotations[2])(qubit)\n",
+        ")\n",
         "datapoint_circuits = tfq.convert_to_tensor([\n",
-        "    cirq.Circuit(\n",
-        "        cirq.rx(random_rotations[0])(qubit),\n",
-        "        cirq.ry(random_rotations[1])(qubit),\n",
-        "        cirq.rz(random_rotations[2])(qubit))\n",
+        "  noisy_preparation\n",
         "] * 2)  # Make two copied of this circuit"
       ],
       "execution_count": 0,
@@ -915,6 +917,73 @@
         "From this plot you can see that the neural network has learned to overcome the systematic miscalibration."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C2RfWismj66S",
+        "colab_type": "text"
+      },
+      "source": [
+        "### 2.6 Verify outputs\n",
+        "Now use the trained model, to correct the qubit calibration errors. With Cirq:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RoIlb7r7j5SY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "def check_error(command_values, desired_values):\n",
+        "  \"\"\"Based on the value in `command_value` see how well you could prepare\n",
+        "  the full circuit to have `desired_value` when taking expectation w.r.t. Z.\"\"\"\n",
+        "  params_to_prepare_output = controller(command_values).numpy()\n",
+        "  full_circuit = noisy_preparation + model_circuit\n",
+        "\n",
+        "  # Test how well you can prepare a state to get expectation the expectation\n",
+        "  # value in `desired_values`\n",
+        "  for index in [0, 1]:\n",
+        "    state = cirq_simulator.simulate(\n",
+        "        full_circuit,\n",
+        "        {s:v for (s,v) in zip(control_params, params_to_prepare_output[index])}\n",
+        "    ).final_state\n",
+        "    expectation = z0.expectation_from_wavefunction(state, {qubit: 0}).real\n",
+        "    print(f'For a desired output (expectation) of {desired_values[index]} with'\n",
+        "          f' noisy preparation, the controller\\nnetwork found the following '\n",
+        "          f'values for theta: {params_to_prepare_output[index]}\\nWhich gives an'\n",
+        "          f' actual expectation of: {expectation}\\n')\n",
+        "\n",
+        "\n",
+        "check_error(commands, expected_outputs)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wvW_ZDwmsws6",
+        "colab_type": "text"
+      },
+      "source": [
+        "The value of the loss function during training provides a rough idea of how well the model is learning. The lower the loss, the closer the expectation values in the above cell is to `desired_values`. If you aren't as concerned with the parameter values, you can always check the outputs from above using `tfq`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aYskLTacs8Ku",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "model([datapoint_circuits, commands])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1144,6 +1213,16 @@
       ],
       "execution_count": 0,
       "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n2WtXnsxubD2",
+        "colab_type": "text"
+      },
+      "source": [
+        "Success: See if you can adapt the `check_error` function from your first model to work with this new model architecture."
+      ]
     }
   ]
 }