Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-12 12:19 UTC

munich-quantum-toolkit / predictor

MQT Predictor - A Tool for Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing

PythonMIT★ 87 stars⑂ 23 forkssince Mar 2022View on GitHub ↗
KindLibraryhow this is determined
#reinforcement-learning#supervised-machine-learning#mqt#qiskit#quantum-compilation#tum

munich-quantum-toolkit/predictor holds a health index of 92 out of 100, placing it in the Excellent band. It scores highest on AI Readiness (97/100) and lowest on Community & Adoption (58/100). It was last updated today. A single contributor accounts for most of its recent work.

92
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

92
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 78 is calibrated to 92 on the published index scale (record calibration 2026-08-02).

Ownership

The Munich Quantum Toolkit (MQT)Organization
103 followers25 public repossince Aug 2024

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPImqt.predictor2.4.01491460 days agomqtcompilationmachine-learningpredictionquantum-computing

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

94Exceptional · 21% of overall

Development activity

100Exceptional
How it's scored
36/36Push recency — last push 0 days ago
36/36Commit cadence — 52/52 weeks with commits
18/18Commit volume — 295 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year295
human_commit_share0.18
days_since_last_push0
active_weeks_last_year52

Release discipline

84Excellent
How it's scored
27/27Ships releases — 14 releases published
36/36Release recency — latest release 60 days ago
12.6/27Release cadence — a release every ~139.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count14
latest_release_tagv2.4.0
releases_from_tagsno
days_since_latest_release60
mean_days_between_releases139.9
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

58Moderate · 17% of overall

Popularity & adoption

43Weak
How it's scored
31.4/60Stars — 87 stars
11.2/25Forks — 23 forks
0/15Watchers — 1 watchers
Inputs used
forks23
stars87
watchers1
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonbelow_threshold

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges9
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesshields.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

36Weak
How it's scored
29/80Monthly downloads — 149 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesmqt.predictor
dependents—
ecosystemspypi
total_downloads—
monthly_downloads149
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

62Moderate · 23% of overall

Maintainer resilience (bus factor)

36Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
4.4/22.5Commit distribution — top contributor authored 80% of commits
12.2/13.5Contributor breadth — 9 contributors
10/10OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled9
top_contributor_share0.804

Issue & PR responsiveness

67Good
How it's scored
31.2/42Issue resolution — 74% of issues closed
25.5/30PR acceptance — 628/739 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
1.5/15OpenSSF Scorecard: Code-Review — Found 5/26 approved changesets -- score normalized to 1
Inputs used
merged_prs628
open_issues16
closed_issues46
prs_merged_7d0
prs_decided_7d0
prs_merged_30d13
prs_decided_30d27
issue_closed_ratio0.742
closed_unmerged_prs111
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

59Moderate
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
14.5/25Owner reach — 103 followers of munich-quantum-toolkit
14.5/25Track record — 25 public repos, account ~2 yr old
Inputs used
followers103
owner_typeOrganization
is_verifiedno
owner_loginmunich-quantum-toolkit
public_repos25
account_age_days765

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 60 days ago
20/20Version history — 14 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesmqt.predictor
ecosystemspypi
any_deprecatedno
min_days_since_publish60

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://mqt.readthedocs.io/projects/predictor
10/10Repository description
10/10Topics — 6 topics
0/10Wiki
Inputs used
topicsreinforcement-learning, supervised-machine-learning, mqt, qiskit, quantum-compilation, tum
has_wikino
homepagehttps://mqt.readthedocs.io/projects/predictor
docs_sitehttps://mqt.readthedocs.io/projects/predictor
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

80Excellent · 16% of overall

Security posture

75Good
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
2.2/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-Review — Found 5/26 approved changesets -- score normalized to 1
2.5/2.5Contributors — project has 6 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
5/5Pinned-Dependencies — all dependencies are pinned
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate7.5
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages92
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:mqt.predictor@2.4.0 runtime dependency closure — what installing the published package pulls in — 92 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

97Exceptional · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — AGENTS.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://mqt.readthedocs.io/llms.txt)
0/40Legible commit history — no data
Inputs used
has_llms_txtyes
llms_txt_urlhttps://mqt.readthedocs.io/llms.txt
legible_history_share—
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes7,306
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.

Verify loop (build / test / typecheck)

100Exceptional
How it's scored
18/18One-command bootstrap — noxfile.py
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — src/mqt/predictor/py.typed
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 5 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 75 of the last 100 commits are automated dependency updates
10/10OpenSSF Scorecard: Pinned-Dependencies — all dependencies are pinned
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesnoxfile.py
has_devcontainerno
has_linter_configyes
typecheck_configssrc/mqt/predictor/py.typed
agent_commit_share0.05
toolchain_manifests—
dependency_bot_commit_share0.75

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (src/mqt/predictor/py.typed)
55/55Manageable file sizes — 0/32 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes27,026
source_files_sampled32
oversized_source_files0

Key facts

87GitHub stars
9contributors
295commits, last 12 months
0days since last push
14releases
1bus factor
16open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Star history carried forward from 2026-04-18: GitHub restricted the stargazers API to repository admins, so it can no longer be collected. The repository has 87 stars today.

More detail

Star and fork history 86 ★ / 23 ⇿
86Stars
23Forks
14Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

Star history is shown as collected on 2026-04-18. GitHub restricted the stargazers API to repository administrators in July 2026, so this series can no longer be extended; the current star total above remains live.

020406080100862362022-062024-072026-08
Major 2Minor 7Patch 5

Each point covers 4 days.

OpenSSF Scorecard 7.5 / 10
7.5aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-09-12 12:19 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 5/26 approved changesets -- score normalized to 1
10Contributorsproject has 6 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
10Pinned-Dependenciesall dependencies are pinned
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 18
RegistryPackageVersion constraintManifest
PyPImqt.bench>=2.0.0pyproject.toml
PyPIqiskit>=1.3.3pyproject.toml
PyPIpytket>=1.29.0pyproject.toml
PyPIpytket>=2.4.0pyproject.toml
PyPIpytket_qiskit>=0.61.0pyproject.toml
PyPIpytket-qiskit>=0.78.0pyproject.toml
PyPIsb3_contrib>=2.0.0pyproject.toml
PyPItqdm>=4.66.0pyproject.toml
PyPIrich>=12.6.0pyproject.toml
PyPIscikit-learn>=1.5.1pyproject.toml
PyPIscikit-learn>=1.7.2pyproject.toml
PyPItensorboard>=2.17.0pyproject.toml
PyPItensorboard>=2.18.0pyproject.toml
PyPIbqskit>=1.2.1pyproject.toml
PyPInumpy>=1.24pyproject.toml
PyPInumpy>=1.26pyproject.toml
PyPInumpy>=2.1pyproject.toml
PyPInumpy>=2.3.2pyproject.toml
All dependencies 186

Full resolved dependency set from the GitHub dependency graph: 12 direct and 174 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIbqskit1.2.1direct
PyPImqt-bench2.2.3direct
PyPInumpy2.4.6direct
PyPInumpy2.5.3direct
PyPIpytket2.18.1direct
PyPIpytket-qiskit0.78.0direct
PyPIqiskit2.5.2direct
PyPIrich15.0.0direct
PyPIsb3-contrib2.9.0direct
PyPIscikit-learn1.9.0direct
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PyPIattrs26.1.0indirect
PyPIbabel2.18.0indirect
PyPIbeautifulsoup44.15.0indirect
PyPIbqskitrs0.4.1indirect
PyPIcertifi2026.7.22indirect
PyPIcffi2.1.1indirect
PyPIcharset-normalizer3.5.1indirect
PyPIclick8.5.0indirect
PyPIcloudpickle3.1.2indirect
PyPIcolorama0.4.6indirect
PyPIcolorlog6.12.0indirect
PyPIcomm0.2.3indirect
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PyPIcuda-pathfinder1.8.1indirect
PyPIcuda-toolkit13.0.3.0indirect
PyPIdebugpy1.8.21indirect
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PyPIhumanize4.16.0indirect
PyPIibm-cloud-sdk-core3.26.1indirect
PyPIibm-platform-services0.77.1indirect
PyPIibm-quantum-schemas0.11.20260824indirect
PyPIidna3.19indirect
PyPIimagesize2.0.1indirect
PyPIimportlib-metadata9.0.1indirect
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PyPIjinja23.1.6indirect
PyPIjoblib1.6.0indirect
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PyPIjsonschema-specifications2025.9.1indirect
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PyPInvidia-cufft12.0.0.61indirect
PyPInvidia-cufile1.15.1.6indirect
PyPInvidia-curand10.4.0.35indirect
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PyPInvidia-cusparselt-cu130.8.1indirect
PyPInvidia-nccl-cu132.30.7indirect
PyPInvidia-nvjitlink13.3.33indirect
PyPInvidia-nvshmem-cu133.4.5indirect
PyPInvidia-nvtx13.0.85indirect
PyPIopenqasm31.0.1indirect
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PyPIplatformdirs4.11.7indirect
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PyPIpytest-cov7.1.0indirect
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PyPIpyyaml6.0.3indirect
PyPIpyzmq27.2.0indirect
PyPIqiskit-ibm-runtime0.49.0indirect
PyPIqiskit-qasm3-import0.6.0indirect
PyPIqwasm1.0.1indirect
PyPIreferencing0.37.0indirect
PyPIrequests2.34.2indirect
PyPIrequests-ntlm1.3.0indirect
PyPIroman-numerals4.1.0indirect
PyPIrpds-py2026.6.3indirect
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PyPIsamplomatic0.21.0indirect
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PyPIscipy1.18.1indirect
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PyPIsix1.17.0indirect
PyPIsnowballstemmer3.1.1indirect
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PyPIsphinx9.1.0indirect
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PyPIsphinx-llm1.0.0indirect
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PyPIsphinxcontrib-bibtex2.7.0indirect
PyPIsphinxcontrib-devhelp2.0.0indirect
PyPIsphinxcontrib-htmlhelp2.1.0indirect
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PyPIstevedore5.9.1indirect
PyPIsymengine0.14.1indirect
PyPIsympy1.14.0indirect
PyPItabulate0.10.0indirect
PyPItensorboard-data-server0.7.2indirect
PyPIthreadpoolctl3.6.0indirect
PyPItomli2.4.1indirect
PyPItorch2.14.0indirect
PyPItornado6.5.8indirect
PyPItraitlets5.16.1indirect
PyPItriton3.8.0indirect
PyPItyping-extensions4.16.0indirect
PyPItyping-inspection0.4.4indirect
PyPIurllib32.7.0indirect
PyPIvirtualenv21.7.8indirect
PyPIwcwidth0.8.3indirect
PyPIwerkzeug3.1.8indirect
PyPIzipp4.1.0indirect
Dependency advisories 0

Installing pypi:mqt.predictor@2.4.0 pulls in 92 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

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Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.