# qiskit-community/qiskit-machine-learning

An open-source library built on Qiskit for quantum machine learning tasks at scale on quantum hardware and classical simulators

Repository: https://github.com/qiskit-community/qiskit-machine-learning
Canonical: https://ross.abutalabs.com/products/qiskit-machine-learning
Homepage: https://qiskit-community.github.io/qiskit-machine-learning/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: qiskit, machine-learning, quantum-computing
Last push: 2026-08-19T21:35:16+00:00

## Health v2 (maintenance only)
Score: 90/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 74, longevity 100
- inputs: {"age_days": 2013, "days_push": 14, "days_rel": 14, "gap_med": 94.5, "n_releases_24m": 7}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1096, forks 448 (observed 2026-08-28T04:03:34.437416+00:00)

## What it is
Qiskit Machine Learning is an open-source Python library built on the Qiskit SDK that provides building blocks for quantum machine learning, including quantum kernels and quantum neural networks. It supports classification, regression, and other ML tasks on both quantum hardware and classical simulators.

## Use cases
- train a quantum neural network classifier on a dataset
- run quantum kernel methods like QSVC for classification
- build hybrid quantum-classical models with PyTorch
- prototype quantum machine learning research experiments
- simulate quantum ML algorithms without quantum hardware
- implement a quantum autoencoder or quantum convolutional neural network

## When to choose
- you want to experiment with quantum machine learning in Python
- you already use Qiskit and need ML building blocks
- you need quantum kernels or quantum neural networks with simulator or hardware backends
- you want PyTorch integration for hybrid quantum-classical models

## When to avoid
- you need classical machine learning with no quantum component
- you need production-scale ML performance today rather than research prototyping
- you work outside the Qiskit/Python quantum ecosystem

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, sdk
- domain: machine-learning, quantum-computing
- platform: python
- tags: quantum-computing, quantum-machine-learning, qiskit, quantum-neural-networks, quantum-kernels

## Member repositories
- qiskit-community/qiskit-machine-learning (main) score 90

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:34.437416+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 2026-08-30T06:47:12.079357+00:00, confidence not recorded.
  - readme: https://github.com/qiskit-community/qiskit-machine-learning (fetched 2026-08-28T04:03:34.437416+00:00, sha 573708b23d64)
  - homepage: https://qiskit-community.github.io/qiskit-machine-learning/ (fetched 2026-08-29T12:50:19.679594+00:00, sha ea3e11c77fe7)
  - registry_pypi: https://pypi.org/pypi/qiskit-machine-learning/json (fetched 2026-08-29T12:50:19.682572+00:00, sha 465dcd679316)
- Data as of 2026-08-30T08:39:29.467469+00:00.