Framework using Butterfly circuits, layer-wise training and parallel parameter-shift reduces QNN training cost to O(log n) circuit evaluations, validated on MIMIC-III clinical data with hardware execution at 16 qubits and simulation at 32.
arXiv preprint arXiv:2003.01695 , year=
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A variational quantum classifier with normalized amplitude embeddings and bounded observables achieves competitive accuracy with improved robustness and stability over classical baselines in safety-critical settings.
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Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
Framework using Butterfly circuits, layer-wise training and parallel parameter-shift reduces QNN training cost to O(log n) circuit evaluations, validated on MIMIC-III clinical data with hardware execution at 16 qubits and simulation at 32.
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SAFE Quantum Machine Learning with Variational Quantum Classifiers
A variational quantum classifier with normalized amplitude embeddings and bounded observables achieves competitive accuracy with improved robustness and stability over classical baselines in safety-critical settings.
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