Stored
- Workload context
Category, goal, accuracy / response-time / scale / audit-level selections you submit through the configurator or the SDK.
- Normalised circuit shape
Qubit count, circuit depth, 1-qubit and 2-qubit gate counts, measurement count, and a deterministic circuit hash. We do not store the source QASM text or your Python source.
- Recommendation output
Top pick, runner-up, full ranking, per-axis fit scores, uncertainty, and the snapshot commit referenced for this run.
- Outcome feedback (optional)
Predicted vs observed fidelity, retry events, selected / rejected backends — submitted only through qlro.log_outcome() or the autolog hook. Empty if you never call them.
- Provenance metadata
Snapshot commit, scoring version, timestamp, qlro SDK version. Lets a third party reproduce the same ranking from the same inputs.
Never collected
- Provider credentials
IBM Quantum API tokens, AWS Braket keys, Azure Quantum secrets, Quantinuum / IonQ / Pasqal / Atom Computing access tokens. We never see them, by design — qlro runs locally against your provider session.
- Generic secrets
API keys, SSH keys, .env values, notebook secret cells, OAuth tokens for unrelated services.
- Local file system
Raw file paths, file contents outside the circuit you explicitly pass, working-directory listings.
- Operating-system environment
Environment variables, shell history, system identifiers, hardware fingerprints.
- Personal identity beyond your sign-in email
We use the email tied to your Google or GitHub sign-in for account and quota accounting. We do not collect a phone number, a billing address, or third-party identity beyond what oauth returns.
Learning use
- ·Stored data may inform the next snapshot release. Snapshots are versioned and pinned by commit hash; old recommendations stay reproducible.
- ·If an outcome you submit contradicts the prediction, that pair becomes a training signal for the next scoring revision. We never overwrite your historical recommendation — we re-rank in a future snapshot only.
- ·We do not sell stored data, share it with vendors, or pass it to a third-party analytics provider beyond the cookie-free Vercel Analytics aggregate used to debug the site.
- ·The stored data is what makes Qlro recommendations citable: the methodology DOI plus the snapshot commit plus the parameters you submitted let any reviewer reproduce the ranking.
Retention and deletion
- ·Recommendation records and outcome feedback are retained for the lifetime of your account by default. We do not auto-delete because the citation chain (decision record → snapshot → paper DOI) breaks if we silently drop runs.
- ·You can delete any individual run or your entire account via /settings → Delete account. Deletion removes the run and outcome rows; aggregate snapshot statistics already published are preserved.
- ·If you require contractual data retention controls (custom TTL, regional storage, customer-managed key, audit log export), that is an Enterprise topic — contact us via the partner channel.