minorminer
Heuristic algorithm to find graph minor embeddings.
Decision gist · record as of 2026-08-14
Yes, if you need graph minor embeddings or clique embeddings on quantum topologies. The package is actively maintained, has no known vulnerabilities, and supports current Python versions (3.10–3.14) with precompiled wheels. License treatment is unclear in the metadata—verify the Apache License 2.0 claim before use in proprietary contexts. Medium install friction due to compiled dependencies is typical for scientific Python packages.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10; scipy and numpy must be installed as runtime dependencies.
- Medium install friction: compiled wheels available for Python 3.10–3.14 on macOS, Linux, and Windows, but requires six runtime dependencies including numpy and scipy.
- Repository is active with a recent release (59 days old) and no known vulnerabilities.
License · maintenance · safety
(unclear) — License treatment is unclear—the description mentions Apache License 2.0 but the metadata lacks formal SPDX or raw license declaration. Verify the actual license terms before use in proprietary or restricted-license contexts.
last release 2026-06-16 (59 days) · last repo commit 2026-06-16 · 54 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 98,037 downloads/mo, #13,112 on PyPI
Alternatives
Verify before relying
from minorminer import find_embedding
triangle = [(0, 1), (1, 2), (2, 0)]
square = [(0, 1), (1, 2), (2, 3), (3, 0)]
embedding = find_embedding(triangle, square, random_seed=10)
print(embedding)- Whether the Apache License 2.0 mentioned in the description is the authoritative license for this package.
- Performance characteristics and scalability limits for large graphs.
- Whether the algorithm guarantees a solution or returns empty when no embedding exists.
What it is and what it does
minorminer is a heuristic tool for finding graph minor embeddings—that is, mappings that embed one graph (the minor) into another (the target) as a connected subgraph. The core function, find_embedding(), implements a configurable algorithm based on published research and accepts parameters to tune execution or constrain the problem. It performs comparably to non-configurable implementations while exposing hooks for research use.
The package also provides find_clique_embedding() for polynomial-time clique embeddings on Chimera, Pegasus, and Zephyr graphs (quantum processor topologies), plus utilities for biclique embeddings. It depends on networkx, numpy, scipy, fasteners, homebase, and dwave-graphs, making it suitable for quantum computing workflows, graph algorithm research, and topology-aware problem mapping.
Use it for
- Embed a logical problem graph into a quantum processor's physical topology for quantum annealing.
- Find clique embeddings on specialized graph architectures (Chimera, Pegasus, Zephyr) in polynomial time.
- Map a minor graph into a target graph for graph theory research or algorithm prototyping.
- Constrain or initialize embeddings with fixed or hinted variable assignments for guided search.
- Validate graph minor relationships or explore embedding feasibility in academic or experimental settings.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need graph minor embeddings or clique embeddings on quantum topologies.
The package is actively maintained, has no known vulnerabilities, and supports current Python versions (3.10–3.14) with precompiled wheels. License treatment is unclear in the metadata—verify the Apache License 2.0 claim before use in proprietary contexts. Medium install friction due to compiled dependencies is typical for scientific Python packages.
Install
minorminer on PyPI
Before you install
Medium install friction: compiled wheels available for Python 3.10–3.14 on macOS, Linux, and Windows, but requires six runtime dependencies including numpy and scipy. Repository is active with a recent release (59 days old) and no known vulnerabilities.
Requires Python >=3.10; scipy and numpy must be installed as runtime dependencies.
License in practice
License treatment is unclear—the description mentions Apache License 2.0 but the metadata lacks formal SPDX or raw license declaration. Verify the actual license terms before use in proprietary or restricted-license contexts.
Quickstart
from minorminer import find_embedding
triangle = [(0, 1), (1, 2), (2, 0)]
square = [(0, 1), (1, 2), (2, 3), (3, 0)]
embedding = find_embedding(triangle, square, random_seed=10)
print(embedding)Verify before relying
- Whether the Apache License 2.0 mentioned in the description is the authoritative license for this package.
- Performance characteristics and scalability limits for large graphs.
- Whether the algorithm guarantees a solution or returns empty when no embedding exists.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 6 packagesdwave-graphsfastenershomebasenetworkxnumpyscipy |
| Maintenance | Actively maintained 59 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 98,037 / month, #13,112 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: minorminer-0.2.22-cp310-cp310-macosx_10_13_x86_64.whl; minorminer-0.2.22-cp310-cp310-macosx_11_0_arm64.whl; minorminer-0.2.22-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; minorminer-0.2.22-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; minorminer-0.2.22-cp310-cp310-win_amd64.whl; minorminer-0.2.22-cp311-cp311-macosx_10_13_x86_64.whl; minorminer-0.2.22-cp311-cp311-macosx_11_0_arm64.whl; minorminer-0.2.22-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; minorminer-0.2.22-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; minorminer-0.2.22-cp311-cp311-win_amd64.whl; minorminer-0.2.22-cp312-cp312-macosx_10_13_x86_64.whl; minorminer-0.2.22-cp312-cp312-macosx_11_0_arm64.whl; minorminer-0.2.22-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; minorminer-0.2.22-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; minorminer-0.2.22-cp312-cp312-win_amd64.whl; minorminer-0.2.22-cp313-cp313-macosx_10_13_x86_64.whl; minorminer-0.2.22-cp313-cp313-macosx_11_0_arm64.whl; minorminer-0.2.22-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; minorminer-0.2.22-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; minorminer-0.2.22-cp313-cp313-win_amd64.whl
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See also Dijkstar · dwave-graphs · planarity · dwave-networkx · cuvs-cu12 · libcuvs-cu12 · keybert · llama-index-embeddings-ollama · llama-index-embeddings-vertex · munkres