qlazy

Quantum Computing Simulator

Feature

• Simple quantum calculation using command line tool is possible.
• Quantum state operation or quantum circuit execution is possible with Python package.
• Support four types of quantum states: 1) quantum state vector, 2) density operator, 3) stabilizer state, 4) matrix product state.
• Other quantum computing services or simulators can be used as backend (The followings are supported).
  • qulacs
  • IBM Quantum(IBMQ)
  • Amazon Braket(LocalSimulator,AWS,IonQ,Rigetti,OQC)
• High-speed quantum circuit execution using GPU is possible (build from source code).

Install

Quick install (PyPI)

$ pip install qlazy

Build from source code (PyPI)

$ pip install --no-binary :all:

Download and build

$ git clone https://github.com/samn33/qlazy.git
$ cd qlazy
$ python setup.py install --user

If you want to use GPU version

$ python setup_gpu.py install --user

Uninstall

$ pip uninstall qlazy

Usage

Command line tool (for state vector simulation)

read file

foo.qc

init 2
h 0
cx 0 1
show

run the circuit

$ qlazy -qc foo.qc
c[00] = +0.7071+0.0000*i : 0.5000 |++++++
c[01] = +0.0000+0.0000*i : 0.0000 |
c[10] = +0.0000+0.0000*i : 0.0000 |
c[11] = +0.7071+0.0000*i : 0.5000 |++++++

print help

$ qlazy -h

interactive mode

$ qlazy
>> init 2
>> h 0
>> cx 0 1
>> show   # show the current quantum state
c[00] = +0.7071+0.0000*i : 0.5000 |++++++
c[01] = +0.0000+0.0000*i : 0.0000 |
c[10] = +0.0000+0.0000*i : 0.0000 |
c[11] = +0.7071+0.0000*i : 0.5000 |++++++
>> m
direction of measurement: z-axis
frq[00] = 49
frq[11] = 51
last state => 00
>> quit

print help

>> help
>> help <item>

Python package

State Vector Simulation (by QState class)

>>> from qlazy import QState
>>> qs = QState(2)
>>> qs.h(0).cx(0,1)
>>> qs.show()
c[00] = +0.7071+0.0000*i : 0.5000 |++++++
c[01] = +0.0000+0.0000*i : 0.0000 |
c[10] = +0.0000+0.0000*i : 0.0000 |
c[11] = +0.7071+0.0000*i : 0.5000 |++++++
>>> md = qs.m(shots=100)
>>> print(md.frequency)
Counter({'00':53,'11':47})

Quantum Circuit Execution (by QCirc and Backend class)

>>> # quantum state vector simulator
>>> from qlazy import QCirc, Backend
>>> bk = Backend(product='qlazy', device='qstate_simulator')
>>> qc = QCirc().h(0).cx(0,1).measure(qid=[0,1], cid=[0,1])
>>> result = bk.run(qcirc=qc, shots=100)
>>> print(result.frequency)
Counter({'00':52, '11':48})

>>> # quantum state vector simulator (GPU)
>>> from qlazy import QCirc, Backend
>>> bk = Backend(product='qlazy', device='qstate_gpu_simulator')
>>> qc = QCirc().h(0).cx(0,1).measure(qid=[0,1], cid=[0,1])
>>> result = bk.run(qcirc=qc, shots=100)
>>> print(result.frequency)
Counter({'00':54, '11':46})

>>> # stabilizer state simulator
>>> bk = Backend(product='qlazy', device='stabilizer_simulator')
>>> qc = QCirc().h(0)
>>> [qc.cx(i,i+1) for i in range(49)]
>>> qc.measure(qid=list(range(50)), cid=list(range(50)))
>>> result = bk.run(qcirc=qc, shots=100)
>>> print(result.frequency)
Counter({'00000000000000000000000000000000000000000000000000': 52, '11111111111111111111111111111111111111111111111111': 48})

>>> # matrix product state simulator
>>> bk = Backend(product='qlazy', device='mps_simulator')
>>> qc = QCirc().h(0)
>>> [qc.cx(i,i+1) for i in range(49)]
>>> qc.measure(qid=list(range(50)), cid=list(range(50)))
>>> result = bk.run(qcirc=qc, shots=100)
>>> print(result.frequency)
Counter({'11111111111111111111111111111111111111111111111111': 57, '00000000000000000000000000000000000000000000000000': 43})

>>> # real quantum computer (IBMQ)
>>> from qlazy import QCirc, Backend
>>> bk = Backend(product='ibmq', device='least_busy')
>>> qc = QCirc().h(0).cx(0,1).measure(qid=[0,1], cid=[0,1])
>>> result = bk.run(qcirc=qc, shots=100)
>>> print(result.frequency)
Counter({'00':47, '11':41, '01':7, '10':5})

Other classes

• DensOp class: for operating density operator.
• Stabilizer class: for operating stabilizer state.
• MPState class: for operating matrix product state.
• Observable class: for specifying observable.
• PauliProduct: for specifying pauli product.

Documents

• Welcome to qlazy's documentations!
  • Tutorial
  • Tutorial (japanese)
  • Python API

Requirements

• Linux (Ubuntu 20.04 LTS)
• Python 3.8
• numpy 1.21.0
• tensornetwork 0.4.6

Optional ...

• pyzx 0.7.0 (to use quantum circuit optimization with ZX-calculus)
• Qulacs 0.3.0 (to use qulacs backend)
• qiskit 0.37.0 (to use IBMQ backend)
• amazon-braket-sdk 1.25.2 (to use amazon braket backend)
• cuda 11 (to use GPU version)

Licence

Apache License 2.0

Author

Sam.N