# QuantumBFS/Yao.jl

Extensible, Efficient Quantum Algorithm Design for Humans.

Repository: https://github.com/QuantumBFS/Yao.jl
Canonical: https://ross.abutalabs.com/products/yaojl
Homepage: https://yaoquantum.org
Language: Julia
License: NOASSERTION
License Family: other
Topics: quantum-computing, quantum-algorithms, quantum-information, quantum-circuit, machine-learning, unitaryhack, yao
Last push: 2026-08-10T00:34:28+00:00

## Health v2 (maintenance only)
Score: 80/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 48, longevity 100
- inputs: {"age_days": 3064, "days_push": 24, "days_rel": 185, "gap_med": 113, "n_releases_24m": 4}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1040, forks 131 (observed 2026-08-28T04:03:20.121398+00:00)

## What it is
Yao is an extensible, efficient open-source framework for quantum algorithm design and quantum circuit simulation written in Julia. It provides composable quantum blocks, built-in automatic differentiation, and CUDA-accelerated batched simulation for quantum information research and education.

## Use cases
- simulate quantum circuits in Julia
- design and prototype quantum algorithms
- compute gradients of quantum circuits for variational algorithms
- build a quantum Fourier transform circuit
- teach quantum computing with hands-on notebooks
- run GPU-accelerated quantum circuit simulation

## When to choose
- you work in Julia and need high-performance quantum circuit simulation
- you need differentiable quantum circuits for quantum machine learning research
- you want an extensible framework to define custom quantum circuit abstractions
- you are teaching or learning quantum computing with runnable examples

## When to avoid
- you need to program an actual quantum computer rather than simulate one
- you prefer Python-based tools like Qiskit or Cirq
- you need large-scale tensor network simulation without the Julia ecosystem
- you require a production-stable release, as the project describes itself as early-release beta

## Facets
- artifact type: library
- maturity: active
- function: simulation, machine-learning, sdk
- domain: quantum-computing, education
- platform: cross-platform
- tags: quantum-circuits, quantum-algorithms, automatic-differentiation, cuda, quantum-simulation, algorithms, julia, gpu

## Member repositories
- QuantumBFS/Yao.jl (main) score 80

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:20.121398+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 2026-08-30T07:03:07.562270+00:00, confidence not recorded.
  - readme: https://github.com/QuantumBFS/Yao.jl (fetched 2026-08-28T04:03:20.121398+00:00, sha 924ba015edca)
  - homepage: https://yaoquantum.org (fetched 2026-08-29T13:04:06.226686+00:00, sha 9dc0ace635a0)
  - site_page: https://docs.yaoquantum.org/dev (fetched 2026-08-29T13:04:06.236042+00:00, sha 0276081b9587)
  - site_page: http://docs.yaoquantum.org/dev (fetched 2026-08-29T13:04:06.239073+00:00, sha 0276081b9587)
- Data as of 2026-08-30T08:39:29.467469+00:00.