The PennyLane-Qrack plugin integrates the Qrack quantum computing framework with PennyLane's quantum machine learning capabilities.
Performance can benefit greatly from following the [Qrack repository "Quick Start" and "Power user considerations."](https://github.com/unitaryfund/qrack/blob/main/README.md#quick-start)
This plugin is addapted from the PennyLane-Qulacs plugin, under the Apache License 2.0, with many thanks to the original developers!
PennyLane is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
unitaryfund/qrack (formerly vm6502q/qrack) is a software library for quantum computing, written in C++ and with GPU support.
PennyLane Catalyst provides optional quantum just-in-time (QJIT) compilation, for improved performance.
- Provides access to a PyQrack simulator backend via the
qrack.simulatordevice - Provides access to a (C++) Qrack simulator backend for Catalyst (also) via the
qrack.simulatordevice
This plugin requires Python version 3.9 or above, as well as PennyLane and the Qrack library.
Installation of this plugin as well as all its Python dependencies can be done using pip (or pip3, as appropriate):
$ pip3 install pennylane-qrackThis step should automatically build the latest main branch Qrack library, for Catalyst support, if Catalyst support is available.
PennyLane-Qrack requires the following libraries be installed:
as well as the following Python packages:
with optional functionality provided by the following Python packages:
- Catalyst >= 0.7
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 test that the PennyLane-Qrack plugin is working correctly you can run
$ make testin the source folder.
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.
PennyLane-Qrack has been directly adapted by Daniel Strano from PennyLane-Qulacs. PennyLane-Qulacs is the work of many contributors.
If you are doing research using PennyLane and PennyLane-Qulacs, please cite their 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/vm6502q/pennylane-qrack
- Issue Tracker: https://github.com/vm6502q/pennylane-qrack/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.
The PennyLane-Qrack plugin is free and open source, released under the Apache License, Version 2.0.