Skip to main content

Pennylane plugin enabling seamless job execution on MonarQ, Calcul Québec’s nonprofit-hosted quantum computer

Project description

pennylane-calculquebec

Pour la version en français, visitez cette page

Table of content

Definitions

Pennylane-CalculQuebec is a PennyLane plugin enabling seamless job execution on MonarQ, Calcul Québec’s nonprofit-hosted quantum computer.

It also offers simulation and pre-processing / post-processing capabilities relative to MonarQ quantum computer.

Pennylane is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.

Calcul Quebec is a non-profit organization that regroups universities from the Province of Quebec and provides computing power to research and academia.

Local installation

Pennylane-calculquebec can be installed using pip:

pip install pennylane-calculquebec

Alternatively, you can clone this repo and install the plugin with the following command from the root of the repo:

pip install -e .

Pennylane and other Python dependencies will be installed automatically during the installation process.

Usage

If you need more information about how to use the plugin, you may read the getting-started jupyter notebook.

Running files

The plugin can be used both in python scripts and Jupyter notebooks. To run a script, you can use the following command:

python base_circuit.py

Dependencies

Python modules

Those packages are installed automatically during the plugin installation process and are necessary for the plugin to work. Here are the links to their respective documentation:

  • For PennyLane, please refer to the PennyLane documentation.

  • Netowkx is a Python graph algorithms library. It is used seemlessly during some of the transpiling steps. Here's the documentation.

  • Numpy is a mathematical library that is used heavily by Pennylane, and the plugin. Here's the documentation.

State of the project and known issues

The plugin is currently in its beta phase and provides access to MonarQ directly through API calls. It also contains capabilities for obtaining benchmarks and machine informations. There are also features that let experimented users change the pre-processing / post-processing behaviour of the device, and create custom pre-processing / post processing steps. There is a simulator device which is currently being developed, but the noise model still needs to be tweaked. The placement and routing phases of the transpiler currently chose wires and couplers by prioritizing best fidelities first, but this does not yield optimal results in terms of errors. The unit-test coverage is still not complete.

Future plans

  • Have 80 % unit test line coverage for each file in the project
  • Integrate circuit paralellization capabilities to run multiple circuits at the same time
  • Add new transpiling steps to the device to improve placement, routing, error mitigation and optimization.
  • Make the MonarQ simulation device available through qml.device

References

Calcul Québec's Wiki provides a lot of information on the plugin, its components and how to use them. You can access it here.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pennylane_calculquebec-0.8.2.tar.gz (268.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pennylane_calculquebec-0.8.2-py3-none-any.whl (66.2 kB view details)

Uploaded Python 3

File details

Details for the file pennylane_calculquebec-0.8.2.tar.gz.

File metadata

  • Download URL: pennylane_calculquebec-0.8.2.tar.gz
  • Upload date:
  • Size: 268.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for pennylane_calculquebec-0.8.2.tar.gz
Algorithm Hash digest
SHA256 6a4200c90af773cc2ea5770ae962d1854cb98036345cf3dbca60f69357f1694c
MD5 6edd4f156ee3303747eeeab775ba5f5a
BLAKE2b-256 b7d349ba7636bd5809b8701fd228b8a4ec84debadefecbcc5b9452cde6c90058

See more details on using hashes here.

Provenance

The following attestation bundles were made for pennylane_calculquebec-0.8.2.tar.gz:

Publisher: python-publish.yml on calculquebec/pennylane-calculquebec

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pennylane_calculquebec-0.8.2-py3-none-any.whl.

File metadata

File hashes

Hashes for pennylane_calculquebec-0.8.2-py3-none-any.whl
Algorithm Hash digest
SHA256 3e8c5a154d335f519744f126c17fe358f3ae422e6f0cb9f072a899957d177567
MD5 0efdd23d6bb01a6521d210ac1704e692
BLAKE2b-256 c316d86342fd50c6f908b315bd6ecbe62c6455551ccd71a3a95789b25791f6e9

See more details on using hashes here.

Provenance

The following attestation bundles were made for pennylane_calculquebec-0.8.2-py3-none-any.whl:

Publisher: python-publish.yml on calculquebec/pennylane-calculquebec

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page