Machine Learning on Adiabatic Quantum Computers (MAQ) is a library of algorithms used to train machine learning models on adiabatic quantum computers.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for prasannadate from gravatar.com prasannadate

Credits

Author: Prasanna Date, Kathleen Hamilton, Robert Patton, Travis Humble, Thomas Potok

License

BSD License (BSD License)

Tags

Adiabatic Quantum Machine Learning Quantum Machine Learning Adiabatic Quantum Computing Quantum Computing Machine Learning

Classifiers

Report project as malware

Download files

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

Source Distribution

maq-0.2.tar.gz (1.7 kB view details)

Uploaded Source

File details

Details for the file maq-0.2.tar.gz.

File metadata

  • Download URL: maq-0.2.tar.gz
  • Upload date:
  • Size: 1.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.7

File hashes

Hashes for maq-0.2.tar.gz
Algorithm Hash digest
SHA256 53603a63a9a6850f42a8ee9f9a4bc5c2b39a442e3c4bb259b84707e580bd4a48
MD5 78c58ea09b3371d5280ab241d7374922
BLAKE2b-256 8c61501d6f06c5562360ec34731c1eddf50bb84b29d63956ed8bb379ef99731e

See more details on using hashes here.

Supported by

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