Quantum-Inspired Optimization via Adaptive Regularization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired optimization aims to leverage quantum mechanics principles to accelerate and improve optimization algorithms. This paper introduces a novel approach utilizing adaptive regularization, inspired by quantum state evolution. We develop a dynamic regularization strength adjustment mechanism, enabling the algorithm to continuously refine its search strategy. The core mechanism involves incorporating a quantum-inspired penalty term that adapts to the solution space, promoting efficient exploration and potentially leading to faster and more robust solutions compared to traditional optimization methods. This work presents a framework for dynamically adjusting regularization during the optimization process, offering a promising path toward enhanced performance in complex optimization problems.

DOI: 10.5281/zenodo.22188618

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