The PennyLane-Cirq plugin integrates the Cirq quantum computing framework with PennyLane's quantum machine learning capabilities.
PennyLane is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
Cirq is a software library for quantum computing.
The plugin documentation can be found here: https://docs.pennylane.ai/projects/cirq.
- Provides access to built-in Cirq backends including
- Provides access to Pasqal's neutral-atom devices via
- Provides access to the simulators qsim and qsimh via the
- Support for all PennyLane core functionality
This plugin requires Python version 3.7 or above, as well as PennyLane
and Cirq. Installation of this plugin, as well as all dependencies, can be done using
$ pip install pennylane-cirq
Alternatively, you can install PennyLane-Cirq from the source code by navigating to the top directory and running:
$ python setup.py install
PennyLane-Cirq requires the following libraries be installed:
- Python >= 3.7
as well as the following Python packages:
To use the qsim and qsimh devices, the qsim-Cirq interface
qsimcirq is required:
It can be installed using
$ pip install qsimcirq
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-Cirq plugin is working correctly you can run
$ make test
in the source folder.
To build the HTML documentation, go to the top-level directory and run:
$ make docs
The documentation can then be found in the
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-Cirq is the work of many contributors.
If you are doing research using PennyLane and PennyLane-Cirq, 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-cirq
- Issue Tracker: https://github.com/PennyLaneAI/pennylane-cirq/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-Cirq plugin is free and open source, released under the Apache License, Version 2.0.