quEEGNet: Quantum AI for Biosignal Processing
This work addresses biosignal analysis for medical or research applications, but it appears incremental as it combines existing quantum and classical methods.
The paper tackles biosignal processing by proposing a hybrid quantum-classical neural network that integrates a variational quantum circuit into deep neural networks for EEG, EMG, and ECoG analysis, achieving state-of-the-art performance with a small number of trainable parameters.
In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specifically, we propose a hybrid quantum-classical neural network model that integrates a variational quantum circuit (VQC) into a deep neural network (DNN) for electroencephalogram (EEG), electromyogram (EMG), and electrocorticogram (ECoG) analysis. We demonstrate that the proposed quantum neural network (QNN) achieves state-of-the-art performance while the number of trainable parameters is kept small for VQC.