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Lecture notes in networks and systems

Hybrid Quantum Convolutional Neural Network Model for Non-Intrusive Sleep Posture Classification from Pressure Mats

K. S. Gayathri, V. J. Kavyasri, C. V. Malavika

Published Aug 2, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-30963-1_6

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In this research, a Hybrid Quantum Convolutional Neural Network (HQCNN) was proposed for Symmetry-Protected Topological (SPT) Phase data Classification. The HQCNN used quantum Convolutional and pooling layers with traditional fully connected networks. The quantum Convolutional (QConv) and quantum pooling (QPool) layers were developed using quantum circuit states. The TensorFlow quantum package was used to add the outcomes of the quantum circuits to dense layers for efficient classification. A different number of quantum filters were used in the HQCNN model and tested. A different number of quantum filters were used in the HQCNN model design. The performance of the HQCNN models with the different number of quantum filters was compared using validation and testing performance. The simulation results showed that the classification accuracy of the proposed HQCNN with Four Quantum Filters (HQCNN-4QF) was superior to other proposed HQCNNs on the SPT phase data Classification. The HQCNN-4QF model achieved the classification accuracy of 99.22 on SPT phase test data.