🤖 AI Summary
This study investigates the applicability of quantum machine learning to multi-class neuronal M-type classification—a key challenge in computational neuroscience and electrophysiological signal analysis. We propose the first quantum kernel method specifically designed for multi-class morphological neuronal classification, and develop a scalable quantum–classical hybrid feature mapping framework integrating parameterized quantum circuits, quantum kernel embedding, and classical support vector machines (SVMs). Experiments are conducted on both synthetic and real neuronal electrophysiological datasets, with model training performed jointly on the Qiskit simulator and IBM Quantum hardware. Results demonstrate an average classification accuracy of 92.4%, outperforming classical SVM by 6.8 percentage points. To our knowledge, this is the first empirical validation of quantum kernel methods achieving superior performance—and suggestive quantum advantage—in multi-class discriminative tasks within neuroscience.