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 two providers to be used with PennyLane: qiskit.basicaer and qiskit.ibm. These provide access to the respective qiskit 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_qiskitTo test that the PennyLane qiskit plugin is working correctly you can run
$ make testin the source folder. Tests restricted to a specific provider can be run by executing make test-aer or make test-ibm.
Getting started
You can instantiate a 'qiskit.basicaer' device for PennyLane with:
import pennylane as qml
dev = qml.device('qiskit.basicaer', 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.basicaer', wires=2, backend='unitary_simulator')To get a current overview what backends are available you can query this by
dev.capabilities()['backend']Running your code on an IBM Quantum Experience simulator or even a real hardware chip is just as easy. Instead of the
device above, you would instantiate a 'qiskit.ibm' device by giving your IBM Quantum Experience token:
import pennylane as qml
dev = qml.device('qiskit.ibm', 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.ibm]
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 ibm 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, 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.
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.
3 maintainers
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