The PennyLane Rigetti plugin allows different Rigetti devices to work with PennyLane --- the wavefunction simulator, the Quantum Virtual Machine (QVM), and Quantum Processing Units (QPUs).
pyQuil is a Python library for quantum programming using the quantum instruction language (Quil) --- resulting quantum programs can be executed using the Rigetti Forest SDK and Rigetti Quantum Cloud Services (QCS).
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://docs.pennylane.ai/projects/rigetti.
- Provides four devices to be used with PennyLane:
rigetti.numpy_wavefunction,rigetti.wavefunction,rigetti.qvm, andrigetti.qpu. These provide access to the pyQVM Numpy wavefunction simulator, pyQuil wavefunction simulator, quantum virtual machine (QVM), and quantum processing units (QPUs) respectively. - All provided devices support all core qubit PennyLane operations and observables.
PennyLane-Rigetti, as well as all required Python packages mentioned above, can be installed via pip:
$ python -m pip install pennylane-rigetti
Make sure you are using the Python 3 version of pip.
Alternatively, you can install PennyLane-Rigetti from the source code by navigating to the top-level directory and running
$ python setup.py install
PennyLane-Rigetti requires the following libraries be installed:
- Python >=3.9
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) which will allow you to compile your quantum code and run on real QPUs. Note that this requires a valid QCS account and the QCS CLI:
To test that the PennyLane-Rigetti plugin is working correctly you can run
$ make testin the source folder.
To build the HTML documentation, go to the top-level directory and run:
$ make docsThe documentation can then be found in the doc/_build/html/ directory.
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-Rigetti is the work of many contributors.
If you are doing research using PennyLane and PennyLane-Rigetti, 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-rigetti
- Issue Tracker: https://github.com/PennyLaneAI/pennylane-rigetti/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-Rigetti is free and open source, released under the BSD 3-Clause license.