Quantum-Inspired Optimization with Dynamic Feedback

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired optimization is a burgeoning field that explores the potential of quantum mechanics to accelerate and improve optimization algorithms. This paper investigates a novel quantum-inspired optimization method, "Dynamic Feedback Quantum Optimization," designed to address the challenges of convergence and solution quality often encountered in traditional optimization techniques. We propose a framework that dynamically adjusts optimization parameters based on the feedback generated during the optimization process, leveraging the principles of quantum mechanics to enhance exploration and reduce local optima. The core mechanism focuses on a quantum-inspired adjustment process that iteratively refines the optimization strategy, resulting in faster convergence and improved solution quality. This work presents a practical implementation demonstrating the efficacy of this approach, offering a potential pathway toward more efficient optimization processes.

DOI: 10.5281/zenodo.22196498

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