A two-qubit HQNN achieves 99.7% synthetic and 97% real accuracy on radar occupancy classification with up to 170x fewer parameters than CNNs, showing structural efficiency via ablation.
Quantum machine learning in feature hilbert spaces,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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quant-ph 3years
2026 3representative citing papers
Classical data noise significantly amplifies the accuracy drop caused by quantum decoherence in a variational quantum classifier.
A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.
citing papers explorer
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Indoor Occupancy Classification using a Compact Hybrid Quantum-Classical Model Enabled by a Physics-Informed Radar Digital Twin
A two-qubit HQNN achieves 99.7% synthetic and 97% real accuracy on radar occupancy classification with up to 170x fewer parameters than CNNs, showing structural efficiency via ablation.
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A Systematic Study of Noise Effects in Hybrid Quantum-Classical Machine Learning
Classical data noise significantly amplifies the accuracy drop caused by quantum decoherence in a variational quantum classifier.
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A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection
A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.