# PennyLaneAI/pennylane

PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.

Repository: https://github.com/PennyLaneAI/pennylane
Canonical: https://ross.abutalabs.com/products/pennylane
Homepage: https://pennylane.ai
Language: Python
License: Apache-2.0
License Family: permissive
Topics: quantum, machine-learning, deep-learning, neural-network, optimization, quantum-computing, quantum-machine-learning, automatic-differentiation, tensorflow, pytorch, autograd, qiskit, cirq, differentiable-computing, quantum-chemistry, qml, jax, python
Last push: 2026-08-26T22:17:15+00:00

## Health v2 (maintenance only)
Score: 92/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 78, longevity 100
- inputs: {"age_days": 3060, "days_push": 7, "days_rel": 68, "gap_med": 45.5, "n_releases_24m": 17}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3441, forks 854 (observed 2026-08-28T04:08:05.056240+00:00)

## What it is
PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. It lets users build and train quantum circuits with automatic differentiation, integrating with frameworks like TensorFlow, PyTorch, JAX, and hardware backends such as Qiskit and Cirq.

## Use cases
- build and simulate quantum circuits in python
- train quantum machine learning models like neural networks
- compute quantum chemistry simulations with differentiable circuits
- run hybrid quantum-classical optimization algorithms
- interface quantum programs with qiskit or cirq backends
- learn quantum computing through tutorials and demos

## When to choose
- you need differentiable quantum programming integrated with PyTorch, TensorFlow, or JAX
- you want a single library spanning quantum ML, chemistry, and algorithms
- you want to run the same code on simulators and real quantum hardware

## When to avoid
- you only need classical machine learning with no quantum component
- you need low-level pulse-level control of quantum hardware
- you want a non-Python quantum SDK

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, simulation, sdk, math
- domain: quantum-computing, machine-learning, deep-learning, chemistry, artificial-intelligence
- platform: python, cross-platform
- tags: quantum-computing, quantum-machine-learning, quantum-chemistry, automatic-differentiation, hybrid-quantum-classical, tensorflow, pytorch, jax, qiskit, cirq

## Member repositories
- PennyLaneAI/pennylane (main) score 92

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:05.056240+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 2026-08-29T18:37:38.418892+00:00, confidence not recorded.
  - readme: https://github.com/PennyLaneAI/pennylane (fetched 2026-08-28T04:08:05.056240+00:00, sha 8cc602497a22)
  - homepage: https://pennylane.ai (fetched 2026-08-29T09:31:35.752463+00:00, sha 1764dc6e208b)
  - registry_pypi: https://pypi.org/pypi/pennylane/json (fetched 2026-08-29T09:31:35.761681+00:00, sha 76bba7fb9010)
- Data as of 2026-08-30T08:39:29.467469+00:00.