A hybrid QCNN that reuses measurements from qubits discarded during pooling reports large accuracy gains on small image benchmarks, but the baseline is not matched in classical capacity.
Positive and negative excursions are treated equally, preventing biased weight updates during back-propagation
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Hybrid Quantum-Classical Learning for Multiclass Image Classification
A hybrid QCNN that reuses measurements from qubits discarded during pooling reports large accuracy gains on small image benchmarks, but the baseline is not matched in classical capacity.