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.
Outputs are centred at the origin, aligning with the most sensitive region of tanh and similar non-linearities
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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.