论文标题
QUEEGNET：用于生物信号处理的量子AI
quEEGNet: Quantum AI for Biosignal Processing
论文作者
论文摘要
在本文中，我们介绍了一个新兴的量子机学习（QML）框架，以帮助经典的深度学习方法用于生物信号处理应用。具体而言，我们提出了一个杂交量子古典神经网络模型，该模型将变异量子电路（VQC）集成到电脑电图（EEG）（EEG），肌电图（EMG）和电皮质图（ECOG）分析的深神经网络（DNN）中。我们证明了拟议的量子神经网络（QNN）达到了最先进的性能，而可训练的参数的数量则保持在VQC中。
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.
