Quantum-Inspired Optimization via Hybrid Neural Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired optimization represents a promising avenue for accelerating the optimization of complex systems, leveraging the potential of quantum mechanics to guide the search process. This paper proposes a novel architecture that intelligently combines classical optimization techniques with quantum-inspired algorithms. We design a hybrid neural network framework that incorporates quantum-like principles, such as quantum-like activation functions and exploration strategies, to enhance the efficiency and effectiveness of optimization. The core claim is that this hybrid approach will significantly accelerate the optimization of complex systems compared to conventional methods. This work explores the integration of these concepts into a neural network architecture, detailing the design of the network's components and their interplay to achieve optimal results.

DOI: 10.5281/zenodo.22186861

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