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About this paper
Alejandro Mayorga-Redondo, José Daniel Viqueira, Diego Alvarez-Estevez, Eduardo Mosqueira-Rey, Andrés Gómez
The authors evaluate a quantum recurrent neural network (QRNN) for classifying breathing windows, processing airflow timestep by timestep while carrying quantum state as memory, and compare it with a classical recurrent neural network (CRNN). On a small, airflow-only apnea-versus-normal task, mean held-out accuracy is 0.8300 (SD 0.0609), versus 0.6900 (SD 0.0898) for CRNN; medium is similar, but CRNN leads on large.
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