A Spiking Neural Network-Quantum Model for Spatiotemporal Data Analysis: An Experimental Framework

The study introduces a novel computational framework combining neuro-inspired information through spiking neural networks (SNNs) and quantum information using quantum kernels to develop quantum machine learning models. The framework uses a 3D brain-inspired SNN model to learn spatiotemporal EEG signals through their dynamic interaction. Spike frequency state vectors are extracted from a 3D SNN and a quantum kernel classifier is used to classify them into predefined classes. The study also proposes a new embedding function to enhance the performance of the quantum kernel
classifier. The performance of the model is evaluated using statistical metrics and cross-validation techniques, demonstrating its superior efficacy on multiple state-of-the-art embedding functions and classical baseline classifiers. The results indicate a clear advantage of having an integrated framework of an SNN and a quantum kernel, which overcomes the limitations of existing classifiers in dealing with spatiotemporal data and enhances the performance of existing SNNs by improving the classification of their complex internal states.