quimb
Quantum information and many-body library.
Decision gist · record as of 2026-08-14
Yes. Quimb is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. It's well-suited if you need tensor network or exact quantum calculations in Python. The 8 runtime dependencies are standard scientific libraries, and the library is mature enough for research use (Beta status since 2018). Install it if you're working on quantum simulations, many-body physics, or tensor network algorithms.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later; some advanced features may require optional dependencies like jax or torch via autoray.
- Low friction install with a pure-Python wheel.
- Actively maintained with a release 4 days ago and last commit on 2026-08-14.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), so you can use, modify, and distribute it freely in commercial and private projects without copyleft obligations.
last release 2026-08-10 (4 days) · last repo commit 2026-08-14 · 657 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 182,371 downloads/mo, #10,101 on PyPI
Alternatives
Verify before relying
pip install quimb
import quimb as qu
import numpy as np
# Construct a simple tensor network
T1 = qu.tensor.Tensor(np.random.randn(2, 3), inds=['a', 'b'])
T2 = qu.tensor.Tensor(np.random.randn(3, 4), inds=['b', 'c'])
tn = T1 | T2 # Contract tensors
result = tn.contract()- Performance characteristics and scalability limits for large tensor networks compared to specialized frameworks
- Availability and maturity of GPU acceleration through jax/torch backends in practice
- Whether slepc integration for eigensolvers requires separate system-level installation
What it is and what it does
Quimb is a Python library for quantum information and many-body physics calculations, split into two main components. The `quimb.tensor` module handles tensor networks of arbitrary geometry—you can construct, manipulate, contract, and optimize hypergraphs of tensors, run algorithms like DMRG and TEBD, and switch between backend array libraries (numpy, jax, torch) via autoray. The core `quimb` module provides exact quantum calculations where states and operators are represented as dense or sparse matrices, letting you find ground and excited states, perform time evolution, compute entanglement measures, and use numba-accelerated routines.
It's designed for researchers and practitioners working on quantum simulations, condensed-matter physics, and quantum information problems. The library handles the bookkeeping of tensor indices and contraction paths automatically, reducing the friction of implementing complex quantum algorithms by hand. Dependencies include numpy, scipy, numba for JIT compilation, cotengra for smart tensor contraction, and several utility libraries.
Use it for
- Simulate ground states and dynamics of quantum many-body systems using DMRG or TEBD on MPS/PEPS ansätze
- Compute entanglement entropy, mutual information, and other quantum information measures on simulated states
- Prototype and optimize tensor network contraction strategies for arbitrary lattice geometries before deploying to production
- Perform exact diagonalization and time evolution of small quantum systems with automatic handling of large tensor spaces
- Combine quantum simulations with jax or torch backends for GPU acceleration and automatic differentiation
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Quimb is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. It's well-suited if you need tensor network or exact quantum calculations in Python. The 8 runtime dependencies are standard scientific libraries, and the library is mature enough for research use (Beta status since 2018). Install it if you're working on quantum simulations, many-body physics, or tensor network algorithms.
Install
quimb on PyPI
Before you install
Low friction install with a pure-Python wheel. Actively maintained with a release 4 days ago and last commit on 2026-08-14. Requires Python 3.11 or later and brings in 8 runtime dependencies including numpy, scipy, numba, and cotengra for tensor contraction.
Requires Python 3.11 or later; some advanced features may require optional dependencies like jax or torch via autoray.
License in practice
Licensed under Apache-2.0 (permissive), so you can use, modify, and distribute it freely in commercial and private projects without copyleft obligations.
Quickstart
pip install quimb
import quimb as qu
import numpy as np
# Construct a simple tensor network
T1 = qu.tensor.Tensor(np.random.randn(2, 3), inds=['a', 'b'])
T2 = qu.tensor.Tensor(np.random.randn(3, 4), inds=['b', 'c'])
tn = T1 | T2 # Contract tensors
result = tn.contract()Verify before relying
- Performance characteristics and scalability limits for large tensor networks compared to specialized frameworks
- Availability and maturity of GPU acceleration through jax/torch backends in practice
- Whether slepc integration for eigensolvers requires separate system-level installation
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesautoraycotengracytoolznumbanumpypsutilscipytqdm |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 182,371 / month, #10,101 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Physics |
Evidence: quimb-1.15.0-py3-none-any.whl
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