---
id: qiskit-algorithms
version: "0.4.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# qiskit-algorithms — Qiskit Algorithms: A library of quantum computing algorithms

License: permissive · Maintenance: active · Downloads: 112.8K/mo

## What it is and what it does

Qiskit Algorithms is a library of quantum computing algorithms designed to run on quantum computers and simulators via the qiskit framework. It provides implementations of variational, optimization, and machine learning algorithms commonly used in quantum computing research and development.

The package depends on qiskit (the core quantum computing SDK), scipy, and numpy for numerical computation. It is no longer officially maintained by IBM but remains available under Apache 2.0 license for community use and extension. Some algorithms support optional third-party optimizers like scikit-quant, SQSnobFit, and nlopt, which can be installed separately as needed.

Use it for:

- Implement variational quantum eigensolvers (VQE) to find ground state energies of quantum systems.
- Run quantum optimization algorithms on quantum hardware or simulators for combinatorial problem-solving.
- Develop quantum machine learning workflows using pre-built algorithm implementations.
- Prototype and test quantum algorithms before deploying to real quantum hardware.
- Extend or customize existing algorithms for specialized quantum computing research.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Qiskit Algorithms provides a library of quantum computing algorithms built on top of qiskit, scipy, and numpy for running on quantum computers and simulators.

Yes, if you are working with qiskit and need standard quantum algorithms. The low install friction and active repository make it straightforward to use. However, be aware that IBM no longer provides official support—this is a community-maintained project. Suitable for research, prototyping, and learning; evaluate your risk tolerance for unsupported code in production use.

## Install

pip install qiskit-algorithms
uv add qiskit-algorithms
poetry add qiskit-algorithms

## Installing qiskit-algorithms

Before you install: Low install friction with a pure-Python wheel distribution. Maintenance is active with recent commits, though the package is no longer officially supported by IBM—it is community-maintained under Apache 2 license at your own risk.

License in practice: Apache 2.0 permissive license allows free use, modification, and distribution with minimal restrictions. You are free to use and extend the code, but IBM no longer provides official support.

Quickstart:

pip install qiskit-algorithms

from qiskit_algorithms import VQE
from qiskit import QuantumCircuit

# Use VQE or other algorithms from the library

Requires Python 3.9 or later; qiskit, scipy, and numpy must be installed as runtime dependencies.

Verify before relying:

- Whether optional dependencies (scikit-quant, SQSnobFit, nlopt) are required for your specific use case or only for certain algorithms.
- Current state of community maintenance and responsiveness to issues given the lack of official IBM support.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 112.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

quantum algorithms library, qiskit algorithms, quantum computing optimization, variational quantum algorithms, quantum machine learning, quantum simulation algorithms, quantum-computing, community-maintained

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