Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-01 00:20 UTC

microsoft / azure-quantum-python

The azure-quantum python package submits jobs to the Azure Quantum service.

PythonMIT★ 161 stars⑂ 113 forkssince Jan 2021View on GitHub ↗

microsoft/azure-quantum-python holds a health index of 96 out of 100, placing it in the Exceptional band. It scores highest on Sustainability & Governance (95/100) and lowest on AI Readiness (67/100). It was last updated today. 7 contributors account for most of its recent work.

96
overall / 100
Exceptional

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.

96
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 83 is calibrated to 96 on the published index scale (record calibration 2026-08-02).

Ownership

MicrosoftOrganization · verified domain
130,784 followers8,337 public repossince Dec 2013

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIazure-quantum3.13.018,6931930 days ago

Metrics by category

Vitality

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

90Excellent · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
22.2/36Commit cadence — 32/52 weeks with commits
16.7/18Commit volume — 72 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year72
human_commit_share0.93
days_since_last_push0
active_weeks_last_year32

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 33 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~38.8 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count33
latest_release_tagazure-quantum_v3.13.0
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases38.8

Community & Adoption

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

72Good · 17% of overall
How it's scored
35.8/60Stars — 161 stars
17.1/25Forks — 113 forks
6.8/15Watchers — 18 watchers
Inputs used
forks113
stars161
watchers18
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
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
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesbadge.fury.io, dev.azure.com
has_pull_request_templateno
How it's scored
57/80Monthly downloads — 18,693 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesazure-quantum
dependents—
ecosystemspypi
total_downloads—
monthly_downloads18,693
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?

95Exceptional · 23% of overall
How it's scored
51.3/54Bus factor — 7 contributor(s) cover half of all commits
19.5/22.5Commit distribution — top contributor authored 13% of commits
13.5/13.5Contributor breadth — 63 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor7
contributors_sampled63
top_contributor_share0.133
How it's scored
36.3/42Issue resolution — 86% of issues closed
24.2/30PR acceptance — 529/656 decided PRs merged
13/13Newcomer PR acceptance — 1/1 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs529
open_issues14
closed_issues89
prs_merged_7d2
prs_decided_7d2
prs_merged_30d3
prs_decided_30d3
issue_closed_ratio0.864
closed_unmerged_prs127
first_time_authors_30d1
first_time_prs_merged_30d1
first_time_prs_decided_30d1
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
25/25Owner reach — 130,784 followers of microsoft
25/25Track record — 8,337 public repos, account ~12 yr old
Inputs used
followers130,784
owner_typeOrganization
is_verifiedyes
owner_loginmicrosoft
public_repos8,337
account_age_days4,677

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 193 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesazure-quantum
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

78Good · 19% of overall
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
16/16Linter config — tox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://learn.microsoft.com/azure/quantum/
10/10Repository description
10/10Topics — 5 topics
10/10Wiki
Inputs used
topicsquantum, azure, azure-sdk-for-python, python, microsoft
has_wikiyes
homepagehttps://learn.microsoft.com/azure/quantum/
docs_sitehttps://learn.microsoft.com/azure/quantum/
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

78Good · 16% of overall
How it's scored
30/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsazure-quantum/requirements-cirq.txt, azure-quantum/requirements-dev.txt, azure-quantum/requirements-pulser.txt, azure-quantum/requirements-qiskit.txt, azure-quantum/requirements-qsharp.txt, azure-quantum/requirements-quil.txt, azure-quantum/requirements.txt, azure-quantum/setup.py
has_codeql_workflowno
has_security_policyyes
has_dependabot_configyes
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages1
unassessed_packages15
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 1 resolved dependencies against OSV. 15 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

67Good · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 87 of 93 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.935
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — tox.ini
11/11Static type checking — azure-quantum/azure/quantum/_client/py.typed
10/10Reproducible environment — Dockerfile
2/10Demonstrated agent practice — 1 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 6 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configsazure-quantum/azure/quantum/_client/py.typed
agent_commit_share0.01
toolchain_manifests—
dependency_bot_commit_share0.06
How it's scored
27/45Type-checkable code — Python with type-check config (azure-quantum/azure/quantum/_client/py.typed)
52.4/55Manageable file sizes — 4/85 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes124,551
source_files_sampled85
oversized_source_files4
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — notebooks, samples
Inputs used
example_dirsnotebooks, samples
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

161GitHub stars
63contributors
72commits, last 12 months
0days since last push
33releases
7bus factor
14open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:azure-quantum@3.13.0; advisories assessed against the repository dependency graph instead
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 113 ⇿
0Stars
113Forks
31Releases

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.

02040608010012011142021-012023-112026-08
Major 0Minor 0Patch 0

Each point covers 6 days.

All dependencies 16

Full resolved dependency set from the GitHub dependency graph: 0 direct and 16 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIazure-core—indirect
PyPIazure-identity—indirect
PyPIazure-storage-blob—indirect
PyPIcirq-core—indirect
PyPIcirq-ionq—indirect
PyPIcryptography—indirect
PyPIdeprecated—indirect
PyPIgraphviz—indirect
PyPImsrest—indirect
PyPInumpy—indirect
PyPIpulser—indirect
PyPIpyquil4.13.1indirect
PyPIpytest—indirect
PyPIpytest-xdist—indirect
PyPIqsharp—indirect
PyPItox—indirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 1 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct. 15 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

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

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

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.