{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"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.","skillfed_tags":["quantum-computing","tensor-networks","many-body-physics"],"use_cases":["Simulate ground states and dynamics of quantum many-body systems using DMRG or TEBD on MPS/PEPS ans\u00e4tze","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"],"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\u2014you 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.\n\nIt'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.","worth_installing":"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."},"id":"quimb","links":{"html":"https://skillfed.io/packages/quimb","md":"https://skillfed.io/packages/quimb.md","pypi":"https://pypi.org/project/quimb/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-10","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"quimb","python_support":"supports_current","summary":"Quantum information and many-body library."},"popularity":{"monthly_downloads":182371,"position":10101,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.15.0"}