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Exploiting quantum chaos diagnostics in QAOA for enhanced hybrid quantum classical deep learning classification.
Javier Villalba-Díez1,2, Juan Carlos Losada-González3
1Fakultät Wirtschaft, Hochschule Heilbronn, Max-Planck-Str.39, Heilbronn, 74081, Baden-Württemberg, Germany. javier.villalba-diez@hs-heilbronn.de.
Scientific Reports
|May 19, 2026
Summary
A new chaos diagnostic enhances quantum classifiers by improving accuracy in limited quantum systems. This method balances circuit expressivity and sensitivity for better performance.
Area of Science:
- Quantum Computing
- Machine Learning
- Complex Systems
Background:
- Hybrid quantum-classical classifiers offer a promising approach for machine learning tasks.
- The Quantum Approximate Optimization Algorithm (QAOA) is a key variational algorithm for near-term quantum devices.
- Understanding and mitigating finite-size effects and sensitivity are crucial for practical quantum machine learning.
Purpose of the Study:
- To repurpose QAOA as a feature map in a hybrid quantum-classical classifier.
- To introduce a chaos-informed diagnostic to enhance classifier performance.
- To investigate the impact of a chaos diagnostic on classifier accuracy across varying qubit numbers.
Main Methods:
- Extracted a scalar chaos feature using Out-Of-Time-Ordered correlators (OTOC) and lognormal modeling.
- Trained two models on MNIST: StandardHybrid (Pauli expectations) and ChaosAwareHybrid (adds OTOC feature).
- Conducted 5-fold cross-validation across different numbers of qubits ([Formula: see text]) and fixed depth ([Formula: see text]).
Main Results:
- The ChaosAwareHybrid model significantly improved test accuracy at [Formula: see text] qubits ([Formula: see text]-[Formula: see text]), with high win-rates (86-100%).
- At [Formula: see text] qubits, the chaos-aware approach showed diminishing returns or negative effects, indicating over-sensitivity.
- The optimal performance was observed at [Formula: see text] qubits ([Formula: see text]), balancing expressivity and sensitivity.
Conclusions:
- A calibrated chaos diagnostic can enhance hybrid quantum-classical classifiers, especially in resource-limited scenarios.
- The chaos diagnostic provides a tunable parameter to optimize the trade-off between circuit expressivity and sensitivity.
- This approach offers a principled method for improving quantum machine learning model performance.
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