PennyLane Forest Plugin
The PennyLane Forest plugin allows different Rigetti devices to work with PennyLane --- the wavefunction simulator, and the Quantum Virtual Machine (QVM).
PennyLane is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
The plugin documentation can be found here: https://pennylane-forest.readthedocs.io/en/latest/.
- Provides three devices to be used with PennyLane:
forest.qvm. These provide access to the pyQVM Numpy wavefunction simulator, Forest wavefunction simulator, and quantum virtual machine (QVM) respectively.
- All provided devices support all core qubit PennyLane operations and observables.
- Provides custom PennyLane operations to cover additional pyQuil operations:
CPHASE. Every custom operation supports analytic differentiation.
- Combine Forest and the Rigetti Cloud Services with PennyLane's automatic differentiation and optimization.
PennyLane-Forest, as well as all required Python packages mentioned above, can be installed via
$ python -m pip install pennylane-forest
Make sure you are using the Python 3 version of pip.
Alternatively, you can install PennyLane-Forest from the source code by navigating to the top-level directory and running
$ python setup.py install
PennyLane-Forest requires the following libraries be installed:
- Python >=3.6
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.
Additionally, if you would like to compile the quantum instruction language (Quil) and run it locally using a quantum virtual machine (QVM) server, you will need to download and install the Forest software development kit (SDK):
Alternatively, you may sign up for Rigetti's Quantum Cloud Services (QCS) to acquire a Quantum Machine Image (QMI) which will allow you to compile your quantum code and run on real quantum processing units (QPUs), or on a preinstalled QVM. Note that this requires a valid QCS account.
To test that the PennyLane-Forest 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-Forest is the work of many contributors.
If you are doing research using PennyLane and PennyLane-Forest, 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-forest
- Issue Tracker: https://github.com/PennyLaneAI/pennylane-forest/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-Forest is free and open source, released under the BSD 3-Clause license.