quEEGNet: Quantum AI for Biosignal Processing

TL;DR

This paper presents quEEGNet, a hybrid quantum-classical neural network that enhances biosignal processing by integrating variational quantum circuits with deep neural networks, achieving high performance with fewer parameters.

Contribution

It introduces a novel hybrid quantum-classical neural network model for biosignal analysis, combining quantum circuits with deep learning to improve efficiency and accuracy.

Findings

Achieves state-of-the-art performance in biosignal classification.

Uses fewer trainable parameters compared to traditional models.

Demonstrates the effectiveness of quantum circuits in biosignal processing.

Abstract

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.