pwegrzyn/pennylane-extra

Plugin to the Quantum Machine Learning library PennyLane adding MEM

PennyLane Extra

The PennyLane Extra plugin adds some new features to PennyLane. The documentation is still under development.

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

Features

• Measurement error mitigation on Qiskit devices

Installation

PennyLane Extra requires both PennyLane and Qiskit. It can be installed via pip:

$ git clone https://github.com/pwegrzyn/pennylane-extra
$ cd pennylane-extra
$ python -m pip install .

Currently only the 0.9.0-dev versions of PennyLane and PennyLane-qiskit are supported. They are specified as dependencies to this package.

Note: The documentation is currently is WiP.

Please refer to the plugin documentation as well as to the PennyLane documentation for further reference.

Getting started

Once PennyLane Extra is installed, you can start using it's featrues right away.

import pennylane as qml
import pennylane_extra as qmle

dev = qml.device("default.qubit", wires=1)

@qml.qnode(dev)
def circuit(params):
    qml.RX(params[0], wires=0)
    qml.RY(params[1], wires=0)
    return qml.expval(qml.PauliZ(0))

with qmle.qiskit_measurement_error_mitigation():
    print(circuit(np.array([0, 0]))

For more details, see the plugin usage guide and refer to the PennyLane documentation.

Contributing

We welcome contributions - simply fork the PennyLane Extra repository, and then make a pull request containing your contribution. All contributers to PennyLane-Extra 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-Extra.

Authors

The plugin is created by G. Frejek and P. Wegrzyn. The original excellent PennyLane framework was started at Xanadu Quantum Technologies Inc.

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, and Nathan Killoran. PennyLane: Automatic differentiation of hybrid quantum-classical computations. 2018. arXiv:1811.04968

Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran. Evaluating analytic gradients on quantum hardware. 2018. Phys. Rev. A 99, 032331

Support

• Source Code: https://github.com/pwegrzyn/pennylane-extra
• Issue Tracker: https://github.com/pwegrzyn/pennylane-extra/issues

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

License

PennyLane Extra is free and open source, released under the Apache License, Version 2.0.