Quantum-Inspired Optimization via Non-Linear Neural Networks

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum-inspired neural networks (QINNs) offer a promising avenue for optimization, leveraging the principles of quantum mechanics to potentially enhance solution quality. This paper investigates a novel approach to QINNs, focusing on the design of a specialized neural network architecture that mimics quantum dynamics to facilitate more efficient exploration of the solution space. We present a comprehensive analysis of the proposed method, detailing its mathematical foundation, computational implementation, and preliminary results demonstrating improved performance on a benchmark optimization problem. The core claim highlights the potential of this approach to surpass existing classical optimization methods.

DOI: 10.5281/zenodo.22521260

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