Quantum Entanglement-Driven Adaptive System Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre yet fundamental property of quantum mechanics, offers a potentially transformative approach to optimization challenges. This paper explores the application of quantum entanglement to design a novel adaptive system optimization method, leveraging its unique characteristics to achieve superior performance. We present a framework predicated on quantum entanglement's capacity for correlated states and instantaneous correlations, demonstrating its potential to overcome limitations of classical optimization techniques. The core mechanism centers around utilizing entangled quantum states to represent and manipulate variables within a system, enabling a fundamentally different approach to problem-solving. We detail the algorithmic design, the underlying mathematical principles, and preliminary experimental results showcasing the benefits of this quantum-enhanced optimization strategy. The paper concludes with a discussion of future research directions and potential applications across diverse fields.

DOI: 10.5281/zenodo.22187985

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