Work

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

Year: 2026

Type: article

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.

Source: Sensors

DOI: 10.3390/s26020568

Authors: Fang Wang, Xiaoqiang Liang, Xingqian Du

Institutions: Xi'an Technological University, China North Industries Group Corporation (China), China Academy of Space Technology

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: Education Department of Shaanxi Province, Natural Science Basic Research Program of Shaanxi Province