# First steps towards quantum machine learning applied to the classification of event-related potentials

arXiv: https://arxiv.org/abs/2302.02648 · HTML: https://mlsanity.com/paper/2302.02648/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Convex optimization using quantum oracles](https://mlsanity.com/paper/1809.00643/) (2018)
- [Building Brain Invaders: EEG data of an experimental validation](https://mlsanity.com/paper/1905.05182/) (2019)
- [Convex Class Model on Symmetric Positive Definite Manifolds](https://mlsanity.com/paper/1806.05343/) (2018)

## Built on by (most cited first, 2 of 2)

- [A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method](https://mlsanity.com/paper/2404.02447/) (2024)
- [Rethinking Generalized BCIs: Benchmarking 340,000+ Unique Algorithmic Configurations for EEG Mental Command Decoding](https://mlsanity.com/paper/2512.02978/) (2025)

## Abstract (arXiv)

Low information transfer rate is a major bottleneck for brain-computer interfaces based on non-invasive electroencephalography (EEG) for clinical applications. This led to the development of more robust and accurate classifiers. In this study, we investigate the performance of quantum-enhanced support vector classifier (QSVC). Training (predicting) balanced accuracy of QSVC was 83.17 (50.25) %. This result shows that the classifier was able to learn from EEG data, but that more research is required to obtain higher predicting accuracy. This could be achieved by a better configuration of the classifier, such as increasing the number of shots.