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Rigetti backend for the PennyLane library

Project description

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Contains the PennyLane Forest plugin. This plugin allows three 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 the Rigetti QCS.

PennyLane is a machine learning library for optimization and automatic differentiation of hybrid quantum-classical computations.


  • Provides four devices to be used with PennyLane: forest.numpy_wavefunction, forest.wavefunction, forest.qvm, and forest.qpu. These provide access to the pyQVM Numpy wavefunction simulator, Forest wavefunction simulator, quantum virtual machine (QVM), and quantum processing unit (QPU) respectively.
  • All provided devices support all core qubit PennyLane operations and observables.
  • Provides custom PennyLane operations to cover additional pyQuil operations: T, S, ISWAP, CCNOT, PSWAP, and many more. Every custom operation supports analytic differentiation.
  • Combine Forest and the Rigetti Cloud Services with PennyLane’s automatic differentiation and optimization.


PennyLane-Forest requires both PennyLane and pyQuil. It can be installed via pip:

$ python -m pip install pennylane-forest

Getting started

Once the PennyLane-Forest plugin is installed, the three provided pyQuil devices can be accessed straight away in PennyLane.

You can instantiate these devices for PennyLane as follows:

import pennylane as qml
dev_numpy = qml.device('forest.numpy_wavefunction', wires=2)
dev_simulator = qml.device('forest.wavefunction', wires=2)
dev_pyqvm = qml.device('forest.qvm', device='2q-pyqvm', shots=1000)
dev_qvm = qml.device('forest.qvm', device='2q-qvm', shots=1000)
dev_qpu = qml.device('forest.qpu', device='Aspen-0-12Q-A', shots=1000)

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-Forest repository, and then make a pull request containing your contribution. All contributers to PennyLane-Forest 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 and pyQuil.


If you are having issues, please let us know by posting the issue on our Github issue tracker.


PennyLane-Forest is free and open source, released under the BSD 3-Clause license.

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