---
id: quimb
version: "1.15.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# quimb — Quantum information and many-body library.

License: permissive · Maintenance: active · Downloads: 182.4K/mo

## 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 above — verify before relying.

Quimb provides Python tools for quantum information and many-body calculations, with specialized support for tensor networks and exact quantum state simulations using numpy and scipy backends.

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

pip install quimb
uv add quimb
poetry add quimb

## Installing quimb

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.

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()

Requires Python 3.11 or later; some advanced features may require optional dependencies like jax or torch via autoray.

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_current
- Install friction: low
- Maintenance: active
- Downloads: 182.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

tensor network quantum, quantum many-body calculations, DMRG TEBD algorithms, quantum state simulation, tensor contraction optimization, quantum entanglement measures, MPS PEPS quantum, exact quantum dynamics, quantum-computing, tensor-networks, many-body-physics

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