OpenSuperLabExperiment selection workbench
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Model card

QNN-4Q-2L

Architecture
4 qubits · 2 variational layers
Inputs
Formula-derived composition descriptors
Output
Critical-temperature estimate with residual interval
Decision use
Candidate ranking before physical measurement
01
Conditional temperature estimate

The model estimates Tc for scientist-prequalified candidates. It does not prove that an arbitrary formula superconducts.

02
Formula-only scope

Phase, pressure, strain, doping and processing can materially change the observed transition.

03
No quantum-advantage claim

The QNN is evaluated against classical baselines. Portfolio utility is measured per fixed experiment budget.

Lab notebook

Saved campaigns

Campaigns saved in this browser. Restoring one brings back its candidate pool, targets, lab policy, calibration, and recorded results.

Nothing saved yet

Use “Save local” after prioritizing a pool and the campaign will appear here.

Campaign setup

Campaign brief

0 candidates loaded
01 · Start

Start from a template

Prefer to write it in your own words? (optional)

Describe the application, operating temperature, Tc margin, slots, effort budget, element policy, and whether the pool was prequalified. The workbench drafts the structured brief below — nothing is applied until you approve it.

Drafting uses your own OpenAI API key. It stays in this browser tab (session storage), is used once to mint a 10-minute token, and is never stored on any server. The templates above need no key at all.

02 · Review & approve

The brief

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Pick a template or write a description. The structured brief appears here for your review before anything changes.