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A hybrid spiking neural network - quantum framework for spatio-temporal data classification: a case study on EEG data

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Veröffentlicht in:EPJ Quantum Technology vol. 12, no. 1 (Dec 2025), p. 130
1. Verfasser: Jha, Ravi Kumar
Weitere Verfasser: Kasabov, Nikola, Bhattacharyya, Saugat, Coyle, Damien, Prasad, Girijesh
Veröffentlicht:
Springer Nature B.V.
Schlagworte:
Feature extraction
Machine learning
Quantum computing
Computers
Quantum phenomena
Data processing
Quantum physics
Datasets
Performance evaluation
Classification
Neural networks
Artificial intelligence
Spatiotemporal data
Brain research
Optimization
Wrist
State vectors
Feature maps
Information processing
Algorithms
Electroencephalography
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Abstract:The study introduces a hybrid computational framework that combines neuro-inspired information processing using spiking neural networks (SNNs) and quantum information processing using quantum kernels to develop quantum-enhanced machine learning models for spatio-temporal data, demonstrated through the classification of EEG data as a case study. In the proposed SNN-quantum computation (SNN-QC) framework, 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 utilising 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 high-order nonlinear feature map that demonstrates improved performance over state-of-the-art feature maps and several machine learning methods across most of the tasks studied. 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 efficacy across multiple binary classifiers. Quantum hardware validation is conducted using both a superconducting IBM-QPU and a high-fidelity noisy simulation that replicates a real QPU. Furthermore, the results demonstrate that the SNN-QC outperforms models that use statistical features rather than features extracted from the SNN, as the 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.
ISSN:2196-0763
DOI:10.1140/epjqt/s40507-025-00443-1
Quelle:Advanced Technologies & Aerospace Database