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Quantum learning with an evidence gate
QSure chooses measurement strategies, allocates state preparations, quantifies prediction uncertainty, is designed to monitor drift, and abstains when the evidence is not strong enough.
Assurance decision
Evidence supports use
RunAnother strategy is stronger
RerouteUncertainty remains too high
AbstainA valid no-run or insufficient-evidence result is a successful assurance decision.
The assurance layer
Quantum workflows can fail for reasons that an accuracy score does not reveal: finite measurements, drift, unstable calibration, compilation overhead, or a stronger conventional alternative.
QSure evaluates those conditions together and keeps the acquisition budget, uncertainty criteria, and selected route visible.
A valid no-run, reroute, or insufficient-evidence result is a successful assurance decision.
Define states, target properties, data splits, reliability gates, and the resource ledger before evaluation.
Evaluate quantum and conventional measurement strategies against strong classical and measured-feature controls.
Turn finite-shot predictions into calibrated sets, drift signals, and explicit evidence thresholds.
Run, measure more, reroute, or abstain while recording the evidence and counted state preparations.
Primary demonstration
The frozen simulator study constructs 112 distinct four-site XY-chain states, classifies three interior phases and identifies a phase-boundary class, and predicts three incompatible signed observables plus derived energy density from finite-shot measurements.
Frozen comparison
112 states · 30 streams
Measured-feature control
macro-F1
0.898
preparations
2,688
Strongest post-hoc control
QSure-selected policy
macro-F1
0.870
preparations
1,344
Passed selection gates
Finite-shot quantum kernel
macro-F1
0.817
preparations
543,744
1 / 30 streams passed every gate
Candidate quantum path
The finite-shot fidelity kernel learned above the four-class prior, yet only one of 30 rerun streams passed every reliability gate and the method did not beat the strongest measured-feature control.
0.817
finite-shot kernel macro-F1
0.935
empirical prediction-set coverage
1 / 30
streams passing every reliability gate
QSure decision
QSure selected the passing single-copy U-statistic plus RBF policy at 384 preparations per state, or 1,344 amortized preparations per held-out state.
0.870
selected-policy macro-F1
1.000
selective accuracy
0.921
phase-boundary abstention
Demonstrated assurance value
Across 3,600 paired simulator trials, a sequential QSure policy was compared with a fixed 2,048-shot policy on twelve independently generated basis-and-column problems.
82.9%
mean simulated shot savings
94.8%
decisions resolved
0 / 3,600
incorrect run or no-run decisions observed
This is a sampling-policy advantage under the stated simulator contract. It is not quantum computational, hardware, wall-time, or cost advantage.
Assurance ledger
Fixed policy
2,048
shots per trial
QSure median
128
median shots across all trials
Controlled simulator sampling-policy advantage
One-sided 95% lower bound on shot savings: 69.5%. Incorrect-decision upper bound: 0.083%.
Quantum advantage: not demonstrated
The NMF audit remains a negative control; hardware, scaling, wall-time, and cost gates are untested.
NMF control
The scaled binary-NMF portfolio tested ranks 4, 6, 8, 10, 12, and 16 with 30 independent bases and three held-out columns per basis.
Exact enumeration, branch-and-bound, rounded nonnegative least squares with local repair, and simulated annealing reached the exact optimum on all 540 evaluated columns.
The present synthetic NMF family is too easy for a credible quantum-advantage demonstration.
Cross-architecture contract
Explore EnviCloud
Use the public QSure demonstration to compare evidence, measurement budgets, uncertainty, rerouting, and abstention.
Compare measurement strategies, uncertainty, resource budgets, rerouting, and abstention in an evidence-aware quantum workflow.
Open demo (opens in a new tab)See how patterns and useful detail emerge in one- and two-dimensional data.
Open demo (opens in a new tab)Calibrate a model and identify influential, uncertain, or poorly constrained inputs.
Open demo (opens in a new tab)A claim is an output
Talk with EnviTrace about a quantum-data learning problem, a hardware validation plan, or an uncertainty-aware ML workflow.
Discuss a QSure study