New mutation operators and directed mutant generation produce more diverse faulty quantum neural network circuits than prior techniques, as shown in experiments.
Qiskit Machine Learn- ing: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
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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 reservoir computing with distributed architectures reduces time-series forecasting errors by up to 78.8% MAE and 72.3% RMSE in NISQ simulations compared to classical methods.
On a dataset of 80 epitopes, a high-entanglement ZZ feature map in a hybrid QNN showed lower training AUAC and higher test-to-training AUAC ratio than lower-entanglement maps while keeping competitive test accuracy.
citing papers explorer
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Efficient Mutation Testing of Quantum Machine Learning Models
New mutation operators and directed mutant generation produce more diverse faulty quantum neural network circuits than prior techniques, as shown in experiments.
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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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Scalable Quantum Reservoir Computing over Distributed Quantum Architectures
Quantum reservoir computing with distributed architectures reduces time-series forecasting errors by up to 78.8% MAE and 72.3% RMSE in NISQ simulations compared to classical methods.
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Exploring the Effects of Entanglement on Quantum Machine Learning of Pathogen Epitope-Receptor Binding
On a dataset of 80 epitopes, a high-entanglement ZZ feature map in a hybrid QNN showed lower training AUAC and higher test-to-training AUAC ratio than lower-entanglement maps while keeping competitive test accuracy.