# quEEGNet: Quantum AI for Biosignal Processing

arXiv: https://arxiv.org/abs/2210.00864 · HTML: https://mlsanity.com/paper/2210.00864/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 65 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 23 of 23)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Quantum generative adversarial networks](https://mlsanity.com/paper/1804.08641/) (2018)
- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [Towards Quantum Machine Learning with Tensor Networks](https://mlsanity.com/paper/1803.11537/) (2018)
- [Quantum Graph Neural Networks](https://mlsanity.com/paper/1909.12264/) (2019)
- [Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016](https://mlsanity.com/paper/2004.06286/) (2020)
- [EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals](https://mlsanity.com/paper/2106.09135/) (2021)
- [Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction](https://mlsanity.com/paper/2005.02870/) (2020)
- [Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders](https://mlsanity.com/paper/1812.06857/) (2018)
- [Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction](https://mlsanity.com/paper/2008.11426/) (2020)
- [Adversarial Deep Learning in EEG Biometrics](https://mlsanity.com/paper/1903.11673/) (2019)
- [Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders](https://mlsanity.com/paper/2009.13453/) (2020)
- [Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework](https://mlsanity.com/paper/2202.08024/) (2022)
- [AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications](https://mlsanity.com/paper/2205.09115/) (2022)
- [Quantum Transfer Learning for Wi-Fi Sensing](https://mlsanity.com/paper/2205.08590/) (2022)
- [Invariant Representations from Adversarially Censored Autoencoders](https://mlsanity.com/paper/1805.08097/) (2018)
- [Disentangled Adversarial Transfer Learning for Physiological Biosignals](https://mlsanity.com/paper/2004.08289/) (2020)
- [AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data](https://mlsanity.com/paper/2112.09796/) (2021)
- [Learning to Learn Quantum Turbo Detection](https://mlsanity.com/paper/2205.08611/) (2022)
- [On Interfacing the Brain with Quantum Computers: An Approach to Listen to the Logic of the Mind](https://mlsanity.com/paper/2101.03887/) (2020)
- [Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems](https://mlsanity.com/paper/2205.08603/) (2022)

## Built on by (most cited first, 1 of 1)

- [QuPCG: Quantum Convolutional Neural Network for Detecting Abnormal Patterns in PCG Signals](https://mlsanity.com/paper/2511.02140/) (2025)

## 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.