ArticleScientific reports2023
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M-type classification.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deep Learning Pose Estimation for Phenotyping of Co-Occurring Hyperkinetic Movement Disorders.Annals of clinical and translational neurology · 2026Article
- Quantum machine learning for predicting anastomotic leak: a clinical study.Scientific reports · 2026Article
- Comparative performance analysis of quantum feature maps for quantum kernel-based machine learning.Scientific reports · 2026Article
- SPCNNet: spiking point cloud neural network for morphological neuron classification.Scientific reports · 2026Article
- Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex.Neuroinformatics · 2025Article
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Authors and funding
3 authors.
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Abstract
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.
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