pennylane
PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Decision gist · record as of 2026-08-14
Yes. PennyLane is actively maintained, has low installation friction, carries no known security vulnerabilities, and is released under a permissive license. It's the right choice if you're doing quantum computing, quantum machine learning, or quantum chemistry work in Python and need a framework that bridges simulation and real hardware. The 15 dependencies are standard scientific packages, and the active community and documentation support make it a solid foundation for quantum research.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or above.
- For GPU-accelerated simulation, additional setup of pennylane-lightning with GPU support may be needed.
- Low friction installation via pip with a pure-wheel distribution.
License · maintenance · safety
Apache-2.0 (permissive) — Released under Apache License 2.0 (permissive), meaning you can use, modify, and distribute PennyLane freely in both open-source and commercial projects with minimal restrictions—just include a copy of the license.
last release 2026-06-26 (49 days) · last repo commit 2026-08-14 · 3,412 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 297,899 downloads/mo, #7,878 on PyPI
Alternatives
Verify before relying
pip install pennylane
import pennylane as qml
from pennylane import numpy as np
# Create a quantum device
dev = qml.device('default.qubit', wires=2)
# Define a quantum circuit
@qml.qnode(dev)
def circuit(params):
qml.RX(params[0], wires=0)
qml.RY(params[1], wires=1)
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0))
result = circuit([0.1, 0.2])- Whether pennylane-lightning is automatically installed or requires separate setup for GPU acceleration
- Performance characteristics and simulation limits for circuit depth or qubit count
- Compatibility details with specific quantum hardware providers beyond the generic integration claim
What it is and what it does
PennyLane is a quantum computing platform that bridges the gap between quantum algorithm research and practical implementation. It provides a Python interface to define quantum circuits, simulate them on classical hardware, and execute them on real quantum devices. The framework treats quantum computing as a differentiable programming problem, allowing you to train quantum circuits like neural networks using automatic differentiation through autograd and autoray.
The package integrates with a broad ecosystem: it depends on scipy, numpy, networkx, and rustworkx for numerical and graph operations; uses pennylane-lightning for high-performance simulation; and supports hardware-agnostic circuit compilation and resource estimation. It's designed for researchers and developers working in quantum machine learning, quantum chemistry, quantum optimization, and general quantum algorithm development, with extensive documentation, tutorials, and a research demo library.
Use it for
- Build and train variational quantum algorithms for optimization or machine learning tasks using gradient-based methods
- Simulate quantum circuits on classical hardware to prototype algorithms before deploying to real quantum devices
- Develop quantum chemistry simulations to study molecular properties and reactions
- Estimate resource requirements and compile circuits for specific quantum hardware platforms
- Explore quantum machine learning models by treating quantum circuits as differentiable layers in hybrid workflows
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
PennyLane is actively maintained, has low installation friction, carries no known security vulnerabilities, and is released under a permissive license. It's the right choice if you're doing quantum computing, quantum machine learning, or quantum chemistry work in Python and need a framework that bridges simulation and real hardware. The 15 dependencies are standard scientific packages, and the active community and documentation support make it a solid foundation for quantum research.
Install
pennylane on PyPI
Before you install
Low friction installation via pip with a pure-wheel distribution. Active maintenance with a release 49 days ago and ongoing commits. Requires Python 3.11 or above. Brings in 15 runtime dependencies including scipy, numpy, and pennylane-lightning, which is manageable for a scientific package of this scope.
Requires Python 3.11 or above. For GPU-accelerated simulation, additional setup of pennylane-lightning with GPU support may be needed.
License in practice
Released under Apache License 2.0 (permissive), meaning you can use, modify, and distribute PennyLane freely in both open-source and commercial projects with minimal restrictions—just include a copy of the license.
Quickstart
pip install pennylane
import pennylane as qml
from pennylane import numpy as np
# Create a quantum device
dev = qml.device('default.qubit', wires=2)
# Define a quantum circuit
@qml.qnode(dev)
def circuit(params):
qml.RX(params[0], wires=0)
qml.RY(params[1], wires=1)
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0))
result = circuit([0.1, 0.2])Verify before relying
- Whether pennylane-lightning is automatically installed or requires separate setup for GPU acceleration
- Performance characteristics and simulation limits for circuit depth or qubit count
- Compatibility details with specific quantum hardware providers beyond the generic integration claim
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 15 packagesscipynetworkxrustworkxautogradappdirsautoraycachetoolspennylane-lightningrequeststomlkittyping_extensionspackagingdiastatic-maltnumpygast |
| Maintenance | Actively maintained 49 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 297,899 / month, #7,878 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: ConsoleIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: POSIX :: LinuxProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Physics |
Evidence: pennylane-0.45.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “quantum computing framework”
- pennylanePennyLane is a quantum computing framework that lets you build,…
- cirq-coreCirq-core provides the foundational framework for writing,…
- qiskit-experimentsQiskit Experiments provides tools for building, running, and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Physics packages
NetworkX provides data structures and algorithms for creating, analyzing, and manipulating graphs and networks, supporting everything from simple undirected graphs to complex directed and weighted networks.
SymPy is a Python library for symbolic mathematics, performing algebraic manipulation, calculus, equation solving, and mathematical expression simplification without numerical approximation.
Pydicom reads, modifies, and writes DICOM medical imaging files in pure Python, with optional NumPy support for pixel data as arrays.
Install it if you work with medical imaging data, DICOM files, or healthcare IT systems.
Albumentations applies image transformations to training data, supporting classification, segmentation, object detection, and pose estimation with a unified API for images, masks, bounding boxes, and keypoints.
Install it if you need a unified, production-grade augmentation API for computer vision tasks.
CoolProp provides thermodynamic and transport property calculations for fluids and fluid mixtures, offering open-source functionality similar to REFPROP.
Provides quaternion representation, manipulation, and rotation operations for 3D geometry and animation, with support for smooth interpolation between orientations.
See also pennylane-lightning · cirq · cirq-core · pytket · qiskit · openfermion · cirq-pasqal · cirq-web · openfermionpyscf · pyscf