Title: Quantum-Inspired Optimization with Hybrid Neural Networks

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum-inspired optimization represents a paradigm shift in the field of optimization, aiming to mimic the inherent properties of quantum mechanics – specifically, entanglement and superposition – to enhance algorithm performance. This paper introduces a novel hybrid neural network architecture designed to exploit these principles, specifically through the utilization of a 'quantum-aware feature map' and a 'quantum-inspired momentum term.' We explore the integration of these mechanisms to improve the efficiency and robustness of optimization algorithms, particularly in complex, high-dimensional problem spaces. The core claim is that this hybrid approach yields significant improvements in convergence speed, solution quality, and adaptability compared to traditional optimization methods. We present a comprehensive analysis of the proposed architecture and demonstrate its effectiveness through illustrative examples.

DOI: 10.5281/zenodo.22219108

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