TR2022-121
quEEGNet: Quantum AI for Biosignal Processing
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- , "quEEGNet: Quantum AI for Biosignal Processing", IEEE Conference on Biomedical and Health Informatics (BHI), DOI: 10.1109/BHI56158.2022.9926814, September 2022.BibTeX TR2022-121 PDF Video Presentation
- @inproceedings{Koike-Akino2022sep,
- author = {Koike-Akino, Toshiaki and Wang, Ye},
- title = {quEEGNet: Quantum AI for Biosignal Processing},
- booktitle = {IEEE Conference on Biomedical and Health Informatics (BHI)},
- year = 2022,
- month = sep,
- publisher = {IEEE},
- doi = {10.1109/BHI56158.2022.9926814},
- issn = {2641-3604},
- isbn = {978-1-6654-8791-7},
- url = {https://www.merl.com/publications/TR2022-121}
- }
- , "quEEGNet: Quantum AI for Biosignal Processing", IEEE Conference on Biomedical and Health Informatics (BHI), DOI: 10.1109/BHI56158.2022.9926814, September 2022.
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MERL Contacts:
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Research Areas:
Artificial Intelligence, Machine Learning, Signal Processing
Abstract:
In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specif- ically, 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.
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