Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification
This study addresses the problem of neuronal type classification for neuroscientists, providing an incremental approach to applying quantum machine learning to real-world data.
The study tackled the challenge of multiclass neuron M type classification using quantum machine learning, achieving similar performance to classical methods with certain advantages in various configurations. The results showed comparable outcomes to classical algorithms, with no specific numbers reported.
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.