mlsanityData to 7 Oct 2026

quEEGNet: Quantum AI for Biosignal Processing

arXiv 2210.00864 ↗First posted 29 Sep 2022quant-ph cs.LG eess.SPcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 65 references that are in the map. First posted on arXiv 29 Sep 2022.

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23 of 65 references.

References · 23

  1. 2018Supervised learning with quantum enhanced feature spacescited by 654
  2. 2018Barren plateaus in quantum neural network training landscapescited by 624
  3. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 616
  4. 2018Quantum generative adversarial networkscited by 128
  5. 2021QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuitscited by 97
  6. 2018Towards Quantum Machine Learning with Tensor Networkscited by 74
  7. 2019Quantum Graph Neural Networkscited by 66
  8. 2020Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016cited by 56
  9. 2021EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signalscited by 28
  10. 2020Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reductioncited by 13
  11. 2018Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoderscited by 11
  12. 2020Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extractioncited by 11
11 more
  1. 2019Adversarial Deep Learning in EEG Biometricscited by 9
  2. 2020Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoderscited by 7
  3. 2022Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Frameworkcited by 7
  4. 2022AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communicationscited by 6
  5. 2022Quantum Transfer Learning for Wi-Fi Sensingcited by 4
  6. 2018Invariant Representations from Adversarially Censored Autoencoderscited by 3
  7. 2020Disentangled Adversarial Transfer Learning for Physiological Biosignalscited by 3
  8. 2021AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Datacited by 3
  9. 2022Learning to Learn Quantum Turbo Detectioncited by 2
  10. 2020On Interfacing the Brain with Quantum Computers: An Approach to Listen to the Logic of the Mindcited by 1
  11. 2022Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systemscited by 1

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025QuPCG: Quantum Convolutional Neural Network for Detecting Abnormal Patterns in PCG Signalscited by 0