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.
- Provides three devices to be used with PennyLane:
qiskit.basicaer,qiskit.aerandqiskit.ibmq. These devices provide access to the various backends. - Supports a wide range of PennyLane operations and expectation values across the providers.
- Combine Qiskit's high performance simulator and hardware backend support with PennyLane's automatic differentiation and optimization.
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:
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-basicaer, make test-aer 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.
Once the PennyLane-Qiskit plugin is installed, the three provided Qiskit devices can be accessed straightaway in PennyLane.
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(qml.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 used by 'qiskit.basicaer'.
Another important difference between the two is that 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_account()
provider = qiskit.IBMQ.get_provider(group='open')
ibmq_16_melbourne = provider.get_backend('ibmq_16_melbourne')
device_properties = ibmq_16_melbourne.properties()
noise_model = basic_device_noise_model(device_properties)
dev = qml.device('qiskit.aer', wires=2, noise_model=noise_model)Please refer to the Qiskit documentation for more information on noise models.
PennyLane-Qiskit supports running PennyLane on IBM Q hardware via the qistkit.ibmq device.
You can choose between different backends - either simulators or real hardware.
import pennylane as qml
dev = qml.device('qiskit.ibmq', wires=2, backend='ibmq_16_melbourne')By default, the qiskit.ibmq device will attempt to use an already active or stored
IBM Q account. If none are available, you may also directly pass your IBM Q API token,
as well as an optional URL:
import pennylane as qml
dev = qml.device('qiskit.ibmq', wires=2, backend='ibmq_qasm_simulator', ibmqx_token="XXX")In order to avoid accidentally publishing your token, it is best to store it using the
qiskit.IBMQ.save_account() function. Alternatively, you can specify the token or URL via the
PennyLane configuration file by
adding a section such as
[qiskit.global] [qiskit.ibmq] ibmqx_token = "XXX" ibmqx_url = "XXX"
Note that, by default, the qiskit.ibmq device uses the simulator backend
ibmq_qasm_simulator, but this may be changed to any of the real backends as given by
dev.capabilities()['backend']Please refer to the plugin documentation as well as to the PennyLane documentation for further reference.
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
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.
Shahnawaz Ahmed, Carsten Blank, Sebastian Boerakker, Christian Gogolin, Josh Izaac.
- Source Code: https://github.com/XanaduAI/pennylane-qiskit
- Issue Tracker: https://github.com/XanaduAI/pennylane-qiskit/issues
If you are having issues, please let us know by posting the issue on our Github issue tracker.
The PennyLane qiskit plugin is free and open source, released under the Apache License, Version 2.0.