# krishnakumarsekar/awesome-quantum-machine-learning

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

Repository: https://github.com/krishnakumarsekar/awesome-quantum-machine-learning
Canonical: https://ross.abutalabs.com/products/awesome-quantum-machine-learning
Language: HTML
License: CC0-1.0
License Family: permissive
Topics: quantum, quantum-computing, quantum-programming-language, machine-learning, artificial-intelligence, artificial-neural-networks, tensorflow, awesome-list, awesome, machine-learning-algorithms, knn-classification, fcm, kmeans, hmm-model, qubits, ant-colony-optimization, ai, quantum-ai, qml
Last push: 2024-05-07T21:09:28+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3416, "days_push": 848, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3652, forks 806 (observed 2026-08-28T04:08:13.066743+00:00)

## What it is
A curated awesome-list of quantum machine learning resources, covering basics, algorithms, study materials, libraries, and projects organized by language. It serves as an educational reference hub linking to papers, tutorials, and project descriptions around the web.

## Use cases
- learn quantum machine learning from scratch
- find quantum ML algorithms and study materials
- discover quantum computing libraries and software
- find quantum machine learning projects to study
- understand the bridge between quantum computing and machine learning
- get started with qubits and quantum algorithms for AI

## When to choose
- you want a curated starting point for learning quantum machine learning
- you need links to algorithms, tutorials, and study materials in one place
- you are exploring quantum computing concepts as applied to AI

## When to avoid
- you need runnable quantum ML code or a framework rather than links
- you need up-to-date, actively maintained documentation
- you want a formal course or structured curriculum with exercises

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, developer-tools, documentation
- domain: quantum-computing, machine-learning, artificial-intelligence, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, quantum-machine-learning, study-materials, curated-resources, qubits, quantum-algorithms

## Member repositories
- krishnakumarsekar/awesome-quantum-machine-learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:13.066743+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 2026-08-29T18:31:40.713186+00:00, confidence not recorded.
  - readme: https://github.com/krishnakumarsekar/awesome-quantum-machine-learning (fetched 2026-08-28T04:08:13.066743+00:00, sha b54f1ff0f7a3)
- Data as of 2026-08-30T08:39:29.467469+00:00.