# Exploring Quantum-Inspired Encoding Strategies in Neuromorphic Systems for Affective State Recognition

## TL;DR

This paper introduces a new quantum-inspired encoding method for spiking neural networks to improve emotion recognition while maintaining low power consumption.

## Contribution

A novel quantum-inspired spiking encoding strategy is proposed, leveraging quantum entanglement and superposition principles for affective state recognition.

## Key findings

- The quantum-inspired encoding method achieves comparable accuracy to existing encoders in emotion classification.
- The proposed method retains the low-power advantage of spiking neural networks.
- Experiments validate the effectiveness of the quantum-inspired encoding paradigm for emotion recognition.

## Abstract

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.