In this paper, we explore the spiking encoding methodology within spiking neural networks for affective state recognition, deriving inspiration from the principles of quantum entanglement. A pioneering encoding strategy is proposed based on the strategic utilization of the quantum mechanical phenomenon of entanglement. By integrating quantum mechanisms into the spike-encoding pipeline, we aim to match the accuracy of existing encoders on emotion-classification tasks while retaining the inherently low-power advantage of spiking neural networks. Notably, leveraging the superposition of quantum bits and their potential quantum entanglement of adjacent values in feature space during encoding calculations, this quantum-inspired encoding paradigm holds substantial promise for augmenting information processing capabilities in brain-like neural networks. Through quantum observation, we derive spike trains characterized by quantum states, thereby establishing a foundation for experimental validation and subsequent investigative pursuits. We conducted experiments on emotion recognition and validated the effectiveness of our method.
Wang et al. (Wed,) studied this question.