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

TL;DR

This paper explores the use of quantum kernel algorithms for multiclass neuron type classification, assessing their performance on real-world neuronal data and comparing it to classical methods.

Contribution

It is the first study to apply quantum machine learning to classify neuron morphologies, highlighting potential advantages and the impact of feature engineering.

Findings

Quantum kernel methods achieved comparable performance to classical algorithms.

Certain configurations showed advantages of quantum methods over classical.

Feature engineering influenced classification accuracy.

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.