PennyLane PyQuest Plugin
PennyLane is a machine learning library for optimization and automatic differentiation of hybrid quantum-classical computations.
PennyLane-PyQuest requires both PennyLane and PyQuest-cffi. It can be installed via
$ pip install git+https://www.github.com/johannesjmeyer/pennylane-pyquest
Once PennyLane-PyQuest is installed, the provided PyQuest-cffi devices can be accessed straight away in PennyLane. The plugin provides both a pure state and a mixed state simulator based on QuEST.
You can instantiate these devices for PennyLane as follows:
import pennylane as qml dev1 = qml.device('pyquest.pure', wires=2) dev2 = qml.device('pyquest.mixed', wires=2)
These devices can then be used just like other devices for the definition and evaluation of QNodes within PennyLane. For more details, see the plugin usage guide and refer to the PennyLane documentation.
We welcome contributions - simply fork the PennyLane-PyQuest repository, and then make a pull request containing your contribution. All contributers to PennyLane-PyQuest will be listed as authors on the releases.
We also encourage bug reports, suggestions for new features and enhancements.
Johannes Jakob Meyer.
- Source Code: https://github.com/johannesjmeyer/pennylane-pyquest
- Issue Tracker: https://github.com/johannesjmeyer/pennylane-pyquest/issues
If you are having issues, please let us know by posting the issue on our GitHub issue tracker.
PennyLane-PyQuest is free and open source, released under the Apache License, Version 2.0.