# tensorflow/quantum

An open-source Python framework for hybrid quantum-classical machine learning.

Repository: https://github.com/tensorflow/quantum
Canonical: https://ross.abutalabs.com/products/tensorflow-quantum
Homepage: https://www.tensorflow.org/quantum
Language: Python
License: Apache-2.0
License Family: permissive
Topics: cirq, google, machine-learning, python, qml, quantum, quantum-computing, quantum-machine-learning, sdk, tensorflow, google-quantum, nisq, quantum-algorithms, quantum-programming, quantum-simulation, api, algorithms, quantum-information, machine-learning-algorithms, machine-learning-library
Last push: 2026-08-05T03:03:27+00:00

## Health v2 (maintenance only)
Score: 84/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 60, longevity 100
- inputs: {"age_days": 2400, "days_push": 28, "days_rel": 189, "gap_med": 58, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2180, forks 664 (observed 2026-08-28T04:06:23.116712+00:00)

## What it is
TensorFlow Quantum is a Python framework for hybrid quantum-classical machine learning that integrates Cirq quantum circuits with TensorFlow/Keras. It provides high-performance quantum circuit simulation, automatic differentiation of circuits, and Keras layers for building quantum ML models.

## Use cases
- build hybrid quantum-classical machine learning models
- simulate parametrized quantum circuits at scale
- train quantum circuits with TensorFlow gradients
- prototype quantum machine learning research
- compute gradients of quantum circuits with parameter shift
- run quantum circuit simulations inside Keras models

## When to choose
- you want to combine Cirq quantum circuits with TensorFlow/Keras workflows
- you need fast simulation of millions of moderately-sized quantum circuits
- you are doing quantum machine learning research on NISQ-era algorithms

## When to avoid
- you need to run on real quantum hardware rather than simulators
- you work outside Linux or with Python versions other than 3.10-3.12
- you need general-purpose classical deep learning without quantum components

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, simulation, sdk
- domain: quantum-computing, machine-learning, deep-learning
- platform: python
- tags: quantum-machine-learning, cirq, tensorflow, keras, hybrid-quantum-classical, quantum-circuits, nisq, qsim, linux

## Member repositories
- tensorflow/quantum (main) score 84

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:23.116712+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:48:44.739424+00:00, confidence not recorded.
  - readme: https://github.com/tensorflow/quantum (fetched 2026-08-28T04:06:23.116712+00:00, sha 36a05df85aa5)
  - homepage: https://www.tensorflow.org/quantum (fetched 2026-08-29T10:28:50.559230+00:00, sha ca675fa1e588)
  - site_page: https://www.tensorflow.org/install (fetched 2026-08-29T10:28:50.563012+00:00, sha 584a762da891)
  - site_page: https://www.tensorflow.org/tfx/api_docs (fetched 2026-08-29T10:28:50.566782+00:00, sha 6977825696fe)
  - site_page: https://www.tensorflow.org/about (fetched 2026-08-29T10:28:50.570162+00:00, sha 817250744d91)
  - site_page: https://www.tensorflow.org/about/case-studies (fetched 2026-08-29T10:28:50.571913+00:00, sha e6701029eec0)
  - site_page: https://www.tensorflow.org/quantum/api_docs/python/tfq (fetched 2026-08-29T10:28:50.573575+00:00, sha 0f8126b9e9eb)
  - site_page: https://www.tensorflow.org/about/bib (fetched 2026-08-29T10:28:50.575112+00:00, sha 5ca8943386b9)
  - site_page: https://www.tensorflow.org/community/contribute (fetched 2026-08-29T10:28:50.568467+00:00, sha 77d394fa76e7)
- Data as of 2026-08-30T08:39:29.467469+00:00.