PennyLane-qiskit 0.0.8

PennyLane plugin for qiskit-terra

PennyLane is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.

qiskit is an open-source compilation framework capable of targeting various types of hardware and a high-performance quantum computer simulator with emulation capabilities, and various compiler plug-ins.

This PennyLane plugin allows to use both the software and hardware backends of qiskit as devices for PennyLane.

Features

• Provides three devices to be used with PennyLane: qiskit.basicaer, qiskit.aer and qiskit.ibmq. These devices provide access to the various backends.

• Supports a wide range of PennyLane operations and expectation values across the providers.

• Combine qiskit high performance simulator and hardware backend support with PennyLane’s automatic differentiation and optimization.

Installation

This plugin requires Python version 3.5 and above, as well as PennyLane and qiskit. Installation of this plugin, as well as all dependencies, can be done using pip:

$ python -m pip install pennylane_qiskit

To test that the PennyLane qiskit plugin is working correctly you can run

$ make test

in the source folder. Tests restricted to a specific provider can be run by executing make test-basicaer, make test-aer or make test-ibmq.

Note

Tests on the IBMQ device can only be run if a ibmqx_token for the IBM Q experience is configured in the PennyLane configuration file. If this is the case, running make test also executes tests on the ibmq device. By default tests on the ibmq device run with ibmq_qasm_simulator backend and those done by the basicaer and aer device are run with the qasm_simulator backend. At the time of writing this means that the test are “free”. Please verify that this is also the case for your account.

Getting started

You can instantiate a 'qiskit.aer' device for PennyLane with:

import pennylane as qml
dev = qml.device('qiskit.aer', wires=2)

This device can then be used just like other devices for the definition and evaluation of QNodes within PennyLane. A simple quantum function that returns the expectation value of a measurement and depends on three classical input parameters would look like:

@qml.qnode(dev)
def circuit(x, y, z):
    qml.RZ(z, wires=[0])
    qml.RY(y, wires=[0])
    qml.RX(x, wires=[0])
    qml.CNOT(wires=[0, 1])
    return qml.expval.PauliZ(wires=1)

You can then execute the circuit like any other function to get the quantum mechanical expectation value.

circuit(0.2, 0.1, 0.3)

You can also change the default device’s backend with

dev = qml.device('qiskit.aer', wires=2, backend='unitary_simulator')

To get a current overview what backends are available you can query this by

dev.capabilities()['backend']

While the device 'qiskit.aer' is the standard go-to simulator that is provided along the qiskit main package installation, there exists a natively included python simulator that is slower but will work usually without the need to check out other dependencies (gcc, blas and so on) which can be sed by 'qiskit.basicaer'. There is an important difference of the two: while 'qiskit.aer' supports a simulation with noise 'qiskit.basicaer' does not.

You can instantiate a noise model and apply it to the device by calling

import pennylane as qml

import qiskit
from qiskit.providers.aer.noise.device import basic_device_noise_model

qiskit.IBMQ.load_accounts()
ibmqx4 = qiskit.IBMQ.get_backend('ibmqx4')
device_properties = ibmqx4.properties()

noise_model = basic_device_noise_model(device_properties)

dev = qml.device('qiskit.aer', wires=2, noise_model=noise_model)

Then all simulations are done with noise. The basic noise model is explained a little at qiskit’s documentation.

Finally one of the more interesting functionality is running your code through the IBM Quantum Experience API. You can choose between different backends having either simulators or real hardware depending on your agreement with IBM. To use this device you would instantiate a 'qiskit.ibmq' device by giving your IBM Quantum Experience token:

import pennylane as qml
dev = qml.device('qiskit.ibmq', wires=2, ibmqx_token="XXX")

In order to avoid accidentally publishing your token, you should better specify it via the PennyLane configuration file by adding a section such as

[qiskit.global]

  [qiskit.ibmq]
  ibmqx_token = "XXX"

It is also possible to define an environment variable IBMQX_TOKEN, from which the token will be taken if not provided in another way.

Per default the backend ibmq uses the simulator backend ibmq_qasm_simulator, but you can change that to be any of the real backends as given by

dev.capabilities()['backend']

Please refer to the documentation of the PennyLane qiskit Plugin as well as well as to the documentation of PennyLane for further reference.

How to cite

If you are doing research using PennyLane, please cite our whitepaper:

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, Carsten Blank, Keri McKiernan and Nathan Killoran. PennyLane. arXiv, 2018. arXiv:1811.04968

Contributing

We welcome contributions - simply fork the repository of this plugin, and then make a pull request containing your contribution. All contributers to this plugin will be listed as authors on the releases.

We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects or applications built on PennyLane.

Authors

Carsten Blank, Sebastian Boerakker, Christian Gogolin, Josh Izaac

Support

• Source Code: https://github.com/carstenblank/pennylane-qiskit

• Issue Tracker: https://github.com/carstenblank/pennylane-qiskit/issues

If you are having issues, please let us know by posting the issue on our Github issue tracker.

License

The PennyLane qiskit plugin is free and open source, released under the Apache License, Version 2.0.