The PennyLane Q# plugin integrates the Q# quantum computing framework with PennyLane's quantum machine learning capabilities.
The Microsoft Quantum Development Kit is an open-source library for quantum programming using the .NET Q# quantum programming language. Resulting quantum programs can be executed using built in local simulators, or via the cloud-based Azure quantum simulator.
PennyLane is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
The documentation can be found here.
- Provides a Microsoft QDK device to be used with PennyLane:
microsoft.QuantumSimulator. This provides access to the local full state simulator. - All provided devices support all core qubit PennyLane operations and observables.
- Provides custom PennyLane operations to cover additional Q# operations, including
T,S,ISWAP,CCNOT,PSWAP, and many more. Every custom operation supports analytic differentiation. - Combine Microsoft Azure quantum simulators with PennyLane's automatic differentiation and optimization.
Installation of this plugin, as well as all dependencies, can be done using
pip:$ python -m pip install pennylane-qsharp
Make sure you are using the Python 3 version of pip.
Alternatively, you can install PennyLane Q# from the source code by navigating to the top directory and running
$ python setup.py install
PennyLane Q# requires the following libraries be installed:
as well as the following Python packages:
If you currently do not have Python 3 installed, we recommend Anaconda for Python 3, a distributed version of Python packaged for scientific computation.
To ensure that PennyLane Q# is working correctly after installation, the test suite can be run by navigating to the source code folder and running
$ make test
To build the HTML documentation, go to the top-level directory and run
$ make docs
The documentation can then be found in the doc/_build/html/ directory.
We welcome contributions - simply fork the PennyLane-Q# repository, and then make a pull request containing your contribution.
All contributers to PennyLane-Q# 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 and the Microsoft QDK.
PennyLane-Q# is the work of many contributors.
If you are doing research using PennyLane and PennyLane-Q#, please cite our paper:
Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, M. Sohaib Alam, Shahnawaz Ahmed, Juan Miguel Arrazola, Carsten Blank, Alain Delgado, Soran Jahangiri, Keri McKiernan, Johannes Jakob Meyer, Zeyue Niu, Antal Száva, and Nathan Killoran. PennyLane: Automatic differentiation of hybrid quantum-classical computations. 2018. arXiv:1811.04968
- Source Code: https://github.com/PennyLaneAI/pennylane-qsharp
- Issue Tracker: https://github.com/PennyLaneAI/pennylane-qsharp/issues
- PennyLane Forum: https://discuss.pennylane.ai
If you are having issues, please let us know by posting the issue on our Github issue tracker, or by asking a question in the forum.
PennyLane-Q# is free and open source, released under the Apache License, Version 2.0.