ArticleScientific reports2026
Exploiting quantum chaos diagnostics in QAOA for enhanced hybrid quantum classical deep learning classification.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The Quantum Approximate Optimization Algorithm (QAOA) is repurposed here as a feature map within a hybrid quantum-classical classifier, augmented by a chaos-informed diagnostic. We extract a scalar chaos feature by evaluating an Out-Of-Time-Ordered correlators (OTOC) along parameter-scaling rays through the trained circuit, computing spacings between local minima, and standardizing them via a pre-fitted lognormal model. To probe finite-size effects, we sweep the number of qubits [Formula: see text] at fixed depth [Formula: see text] and train two models on a balanced 1,000-sample MNIST subset: a StandardHybrid using the [Formula: see text] local Pauli-[Formula: see text] expectations, and a ChaosAwareHybrid which appends the OTOC-derived scalar. We perform multi-run, 5-fold cross-validation with a paired design (identical seeds/folds across models) and report mean±SD, paired mean differences Δ, 95% t- and bootstrap CIs, exact permutation/sign tests, win-rates (Wilson 95% CI), and paired effect sizes. Across [Formula: see text] for [Formula: see text], the chaos-aware variant significantly improves test accuracy at [Formula: see text] with [Formula: see text]-[Formula: see text], all 95% CIs excluding zero, permutation [Formula: see text], high win-rates (86-100%), and large paired effects ([Formula: see text]-2.3). At [Formula: see text] the effect reverses ([Formula: see text], 2% win-rate, [Formula: see text]), indicating over-sensitivity. The best average accuracy occurs at [Formula: see text] ([Formula: see text]; [Formula: see text]; 100% wins). Per-epoch panels (train/val/test; mean±1 SD) reveal a "Goldilocks" width at which expressivity and sensitivity are balanced. These results show that a calibrated chaos diagnostic can enhance hybrid quantum-classical classifiers in resource-limited regimes and provide a principled knob to match circuit expressivity to many-body sensitivity.
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