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IEEE International Conference on Smart Information Systems and Technologies (SIST)

Hybrid Quantum-Classical Neural Network Framework for Sleep Efficiency Classification

Mirwais Mohebi, Aiman Bekturganova, Sarsenbek Zhussupbekov, Jandos Amankulov, Assiya Boltaboyeva, Nurdaulet Tasmurzayev

Published May 13, 2026• 0 citations
Publisher
DOI
10.1109/sist61674.2026.11596027

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Sleep staging is an important task in the field of medicine and is significant for diagnosing sleep disorders and assessing overall health. Classical machine learning has made significant progress in this field, and with the development of quantum computing, quantum machine learning (QML) provides a new model for solving complex data classification tasks. In this paper, we propose a hybrid quantum-classical neural network (HQCNN) architecture based on electroencephalogram (EEG) single-channel signals for automating the sleep staging task. The classical part uses a convolutional neural network (CNN) and a long short-term memory network (LSTM) to extract features of the EEG signal, while the quantum part utilizes variational quantum circuit (VQC) for feature mapping and classification. We evaluated the model on different datasets. The results show that the model is satisfactory for classification. This indicates that the combination of quantum computing and machine learning has great potential and provides new research directions and technical approaches for developing more efficient computational models and dealing with complex data problems.
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