Skip to main content

Open-source Python SDK for photonic quantum computation.

Project description

Tests Docs Pyversions

Lightworks

Lightworks is an open-source Python SDK, designed for the encoding of linear optic circuits for application in photonic quantum computing. These circuits can be packaged with the other SDK components to create quantum jobs for execution on photonic hardware. Lightworks focuses on discrete-variable quantum computing, and can be utilized for both qubit and boson sampling paradigms.

Included within Lightworks is also an emulator, allowing users to evaluate the operation and performance of a particular configuration before hardware execution. There is a number of simulation objects, each offering a differing functionality, ranging from direct quantum state evolution to replicating the typical sampling process from a photonic system. The emulator also supports complex photonic specific noise modelling, providing a valuable insight into the effect of imperfections in photon generation, QPU programming, and detectors, on a target algorithm.

Usage

Python 3.10+ is required.

Lightworks can be installed through pip using the command:

pip install lightworks

Documentation

Documentation of this package is hosted at: https://aegiq.github.io/lightworks/

Contributing

Contributions to Lightworks can be made via a pull request. If you have an idea for a feature that you'd like to implement it may be best to first raise this in the issues sections, as it may be the case that this is already in development internally or is potentially incompatible with the existing Lightworks framework.

Some things to keep in mind before contributing:

  1. Any pull requests should currently be made to the development branch and not main.
  2. We aim to follow the Google Python style guide (https://google.github.io/styleguide/pyguide.html) including their proposed doc strings format.
  3. The existing unit tests should be used to ensure the core functionality of Lightworks remains intact. Additionally, any new features should ideally include a set of tests.
  4. Type hints are used throughout the code to indicate the expected inputs and return for each class and function within Lightworks. These are also used for generating the Sphinx documentation.
  5. Where possible, a line limit of 80 is used across the code.

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

lightworks-1.3.2.tar.gz (89.5 kB view details)

Uploaded Source

Built Distribution

lightworks-1.3.2-py3-none-any.whl (105.1 kB view details)

Uploaded Python 3

File details

Details for the file lightworks-1.3.2.tar.gz.

File metadata

  • Download URL: lightworks-1.3.2.tar.gz
  • Upload date:
  • Size: 89.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for lightworks-1.3.2.tar.gz
Algorithm Hash digest
SHA256 58dd77787341b83c93787e49901d1c90f64edb3691e12847d39c8fb81afb6c07
MD5 70373389da0032269052fdaea9019172
BLAKE2b-256 84c70f1d609e6cfda328ad5645d67680684191a5adb0543051518705ffbaed7f

See more details on using hashes here.

File details

Details for the file lightworks-1.3.2-py3-none-any.whl.

File metadata

  • Download URL: lightworks-1.3.2-py3-none-any.whl
  • Upload date:
  • Size: 105.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for lightworks-1.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 8d7a10e7b6d94c219ad194672eb3ebf74580744fd7ed0716acafc57729c22a81
MD5 11adbfc035c0cfc5b01a0e62cec829d7
BLAKE2b-256 4dfa6b26de65a0785df63aa4b069ee6d6c7f0658bed3354af201bae2da9e6e2d

See more details on using hashes here.

Supported by

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