Title: Quantum-Inspired Algorithm for Non-Linear Matrix Optimization

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum-inspired algorithms represent a novel approach to optimization by mimicking the principles of quantum mechanics. This paper explores the potential of leveraging quantum-like phenomena, specifically quantum tunneling, to accelerate the convergence of algorithms used for non-linear matrix optimization. We present a new algorithm, the Quantum-Driven Optimization Framework (QDOF), that incorporates a quantum-inspired search strategy to navigate complex, non-linear landscapes. The core mechanism focuses on identifying and exploiting quantum tunneling, which is hypothesized to provide a significant speedup compared to classical methods. This investigation delves into the theoretical underpinnings of quantum tunneling and its relevance to optimization challenges, offering a potential pathway for enhanced efficiency in a wide range of applications. The paper concludes with a detailed examination of the QDOF algorithm, its performance evaluations, and potential future directions.

DOI: 10.5281/zenodo.22156259

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