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.
Noise-induced barren plateaus in variational quantum algorithms
2 Pith papers cite this work. Polarity classification is still indexing.
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TUSQ reduces redundant work in noisy quantum simulations via error tallying, commutation, importance sampling, and depth-first tree traversal with compute/uncompute reuse, reporting large speedups over Qiskit, CUDA-Q, and TQSim on 198 benchmarks.
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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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Noisy Quantum Simulation Using Tracking, Uncomputation and Sampling
TUSQ reduces redundant work in noisy quantum simulations via error tallying, commutation, importance sampling, and depth-first tree traversal with compute/uncompute reuse, reporting large speedups over Qiskit, CUDA-Q, and TQSim on 198 benchmarks.