A Hybrid Spiking Neural Network-Quantum Classifier Framework: A Case Study Using EEG Data

The study introduces a hybrid computational framework that combines neuro-inspired information processing using spiking neural networks (SNN) and quantum information processing using quantum kernels to develop quantum-enhanced machine learning models, demonstrated through the classification of EEG data as a case study. In the proposed SNN-quantum kernel classifier (SNN-QC), SNN with spike time information representation is employed to learn spatio-temporal interactions (EEG recorded from multiple channels over time). Frequency-based (rate-based) information as spike frequency state vectors are extracted from the SNN and classified using a quantum classifier. In the latter part, we use the quantum kernel approach utilizing feature maps for classification tasks. The proposed SNN-QC is demonstrated on a benchmark EEG dataset to classify three distinct wrist movement tasks in six binary classification setups as a proof of concept. We introduce a novel feature map with high-order nonlinearity, which has outperformed current state-of-the-art feature maps and various machine learning methods in most of the case studies. Furthermore, the role of hyperparameters for enhanced feature maps is also highlighted. The performance of SNN-QC is evaluated using statistical metrics and cross-validation techniques, demonstrating its 1 efficacy across multiple binary classifiers. An experimental validation is also performed on an IBM QPU. The results demonstrate that the SNN-QC significantly outperforms the models that use statistical features rather than features extracted from the SNN as SNN accounts for the temporal interaction between the spatio-temporal input variables. Finally, we conclude that the SNN-QC offers a potential pathway for developing more accurate neuromorphic-quantum enhanced systems that are both energy-efficient and biologically-inspired, well-suited for dealing with spatio-temporal data.