❗ This plugin will not be supported in newer versions of PennyLane. It is compatible with versions of PennyLane up to and including 0.34❗
The PennyLane-Honeywell plugin provides the ability to use Honeywell Quantum Solutions' ion-trap quantum computing hardware with PennyLane.
PennyLane provides open-source tools for quantum machine learning, quantum computing, quantum chemistry, and hybrid quantum-classical computing.
Honeywell Quantum Solutions provides access to ion-trap quantum computing hardware over the cloud.
Note
The PennyLane-Honeywell plugin is currently in beta release. Expect some features to be updated or change in the future.
The plugin documentation can be found here: PennyLane-Honeywell.
- Provides a PennyLane device
honeywell.hqswhich can be used to access Honeywell Quantum Solutions' online hardware API. - Supports core PennyLane operations such as qubit rotations, Hadamard, basis state preparations, etc.
PennyLane-Honeywell only requires PennyLane for use, no additional external frameworks are needed.
The plugin can be installed via pip:
$ python3 -m pip install pennylane-honeywell
Alternatively, you can install PennyLane-Honeywell from the source code by navigating to the top directory and running
$ python3 setup.py install
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 ensure that PennyLane-Honeywell is working correctly after installation, the test suite can be run by navigating to the source code folder and running
$ make test
To build the HTML documentation, go to the top-level directory and run
$ make docs
The documentation can then be found in the doc/_build/html/ directory.
Once PennyLane-Honeywell is installed, available Honeywell devices can be accessed straight
away in PennyLane. However, the user will need access credentials for the Honeywell Quantum Solutions (HQS) platform in
order to use these remote devices. These credentials should be provided to PennyLane via a
configuration file or environment variable.
Specifically, the variable HQS_TOKEN must contain a valid access key for HQS's online platform.
You can instantiate the HQS device class for PennyLane as follows:
import pennylane as qml
dev1 = qml.device("honeywell.hqs", "machine_name", wires=2)where machine_name is the specific name of the online device you'd like to access. Contact Honeywell Quantum
Solutions to receive platform access and machine names.
HQS devices can then be used just like other devices for the definition and evaluation of quantum circuits within PennyLane. For more details and ideas, see the PennyLane website and refer to the PennyLane documentation.
We welcome contributions—simply fork the PennyLane-Honeywell repository, and then make a pull request containing your contribution. All contributers to PennyLane-Honeywell will be listed as contributors 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 Honeywell Quantum Solutions' machines.
PennyLane-Honeywell is the work of many contributors.
If you are doing research using PennyLane, please cite our papers:
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, Nathan Killoran. PennyLane: Automatic differentiation of hybrid quantum-classical computations. 2018. arXiv:1811.04968
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran. Evaluating analytic gradients on quantum hardware. 2018. Phys. Rev. A 99, 032331
- Source Code: https://github.com/PennyLaneAI/pennylane-honeywell
- Issue Tracker: https://github.com/PennyLaneAI/pennylane-honeywell/issues
If you are having issues, please let us know by posting the issue on our GitHub issue tracker.
PennyLane-Honeywell is free and open source, released under the Apache License, Version 2.0.