# Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification

arXiv: https://arxiv.org/abs/2502.06281 · HTML: https://mlsanity.com/paper/2502.06281/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [A rigorous and robust quantum speed-up in supervised machine learning](https://mlsanity.com/paper/2010.02174/) (2020)
- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [Training Quantum Embedding Kernels on Near-Term Quantum Computers](https://mlsanity.com/paper/2105.02276/) (2021)
- [Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation](https://mlsanity.com/paper/2211.08737/) (2022)
- [An All-Pair Quantum SVM Approach for Big Data Multiclass Classification](https://mlsanity.com/paper/1704.07664/) (2017)
- [Feeding the human brain model](https://mlsanity.com/paper/1706.07220/) (2017)

## Built on by (most cited first, 4 of 4)

- [Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning](https://mlsanity.com/paper/2508.00024/) (2025)
- [Deep Learning Pose Estimation for Multi-Label Recognition of Combined Hyperkinetic Movement Disorders](https://mlsanity.com/paper/2602.00163/) (2026)
- [Rethinking Generalized BCIs: Benchmarking 340,000+ Unique Algorithmic Configurations for EEG Mental Command Decoding](https://mlsanity.com/paper/2512.02978/) (2025)
- [A Multiclass Quantum Aligned Centroid Kernel](https://mlsanity.com/paper/2607.19782/) (2026)

## Abstract (arXiv)

The functional characterization of different neuronal types has been a longstanding and crucial challenge. With the advent of physical quantum computers, it has become possible to apply quantum machine learning algorithms to translate theoretical research into practical solutions. Previous studies have shown the advantages of quantum algorithms on artificially generated datasets, and initial experiments with small binary classification problems have yielded comparable outcomes to classical algorithms. However, it is essential to investigate the potential quantum advantage using real-world data. To the best of our knowledge, this study is the first to propose the utilization of quantum systems to classify neuron morphologies, thereby enhancing our understanding of the performance of automatic multiclass neuron classification using quantum kernel methods. We examined the influence of feature engineering on classification accuracy and found that quantum kernel methods achieved similar performance to classical methods, with certain advantages observed in various configurations.