{"id":18565252,"url":"https://github.com/rigetti/quantumflow","last_synced_at":"2025-10-26T09:51:11.262Z","repository":{"id":96409636,"uuid":"155748412","full_name":"rigetti/quantumflow","owner":"rigetti","description":"QuantumFlow: A Quantum Algorithms Development Toolkit","archived":false,"fork":false,"pushed_at":"2019-06-03T03:20:31.000Z","size":413,"stargazers_count":95,"open_issues_count":0,"forks_count":24,"subscribers_count":18,"default_branch":"master","last_synced_at":"2024-02-15T12:32:56.644Z","etag":null,"topics":["automatic-differentiation","quantum-algorithms","quantum-computing","quantum-machine-learning"],"latest_commit_sha":null,"homepage":"https://quantumflow.readthedocs.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rigetti.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2018-11-01T17:04:32.000Z","updated_at":"2024-01-17T09:12:33.000Z","dependencies_parsed_at":"2023-07-17T01:45:15.630Z","dependency_job_id":null,"html_url":"https://github.com/rigetti/quantumflow","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rigetti%2Fquantumflow","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rigetti%2Fquantumflow/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rigetti%2Fquantumflow/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rigetti%2Fquantumflow/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rigetti","download_url":"https://codeload.github.com/rigetti/quantumflow/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223424553,"owners_count":17142786,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["automatic-differentiation","quantum-algorithms","quantum-computing","quantum-machine-learning"],"created_at":"2024-11-06T22:18:07.619Z","updated_at":"2025-10-26T09:51:06.223Z","avatar_url":"https://github.com/rigetti.png","language":"Python","funding_links":[],"categories":["Quantum algorithms"],"sub_categories":[],"readme":"\u003eNotice: This is research code that will not necessarily be maintained to\n\u003esupport further releases of Forest and other Rigetti Software. We welcome\n\u003ebug reports and PRs but make no guarantee about fixes or responses.\n\n# QuantumFlow: A Quantum Algorithms Development Toolkit\n\n[![Build Status](https://travis-ci.org/rigetti/quantumflow.svg?branch=master)](https://travis-ci.org/rigetti/quantumflow)\n\n## Installation for development\n\nIt is easiest to install QuantumFlow's requirements using conda.\n```\ngit clone https://github.com/rigetti/quantumflow.git\ncd quantumflow\nconda install -c conda-forge --file requirements.txt\npip install -e .\n```\n\nYou can also install with pip. However some of the requirements are tricky to install (notably tensorflow \u0026 cvxpy), and (probably) not everything in QuantumFlow will work correctly.\n```\ngit clone https://github.com/rigetti/quantumflow.git\ncd quantumflow\npip install -r requirements.txt\npip install -e .\n```\n\n## Example\nTrain the QAOA algorithm, with back-propagation gradient descent, to perform\nMAXCUT on a randomly chosen 6 node graph. \n\n```bash\n./examples/qaoa_maxcut.py --verbose --steps 5 --nodes 6 random\n```\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frigetti%2Fquantumflow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frigetti%2Fquantumflow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frigetti%2Fquantumflow/lists"}