qiskit-algorithms
Qiskit Algorithms: A library of quantum computing algorithms
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; qiskit, scipy, and numpy must be installed as runtime dependencies.
- 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 · maintenance · safety
Apache-2.0 (permissive) — 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.
last release 2025-08-29 (350 days) · last repo commit 2026-07-21 · 188 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 112,810 downloads/mo, #12,358 on PyPI
Alternatives
Verify before relying
pip install qiskit-algorithms
from qiskit_algorithms import VQE
from qiskit import QuantumCircuit
# Use VQE or other algorithms from the library- 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.
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 on this page. Verify before relying on it.
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
qiskit-algorithms on PyPI
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.
Requires Python 3.9 or later; qiskit, scipy, and numpy must be installed as runtime dependencies.
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 libraryVerify 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 the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesqiskitscipynumpy |
| Maintenance | Actively maintained 350 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 112,810 / month, #12,358 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Environment :: ConsoleIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Topic :: Scientific/Engineering |
Evidence: qiskit_algorithms-0.4.0-py3-none-any.whl
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See also qiskit · qiskit-experiments · qiskit-terra · quimb · qiskit-aer · qiskit-ibm-runtime · cirq-core · cirq-pasqal · ibm-quantum-schemas · liboqs-python