Quantum Entanglement Optimization for Parameter Tuning in Non-Local Hidden-State Computation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement offers a fundamentally different approach to computation compared to classical approaches, potentially enabling significant speedups and enhanced security. This paper explores the application of quantum entanglement optimization to parameter tuning within non-local hidden-state computation, a challenging area with significant potential for advancements. We propose a novel framework that dynamically adjusts computational parameters based on inherent entanglement properties, aiming to improve both computational efficiency and accuracy. The core mechanism relies on exploiting non-locality to guide parameter adjustments, leading to a more robust and adaptable optimization strategy. This research investigates the impact of this dynamic parameter adjustment on various non-local hidden-state computations, demonstrating a tangible improvement in performance metrics. The paper concludes with a discussion of the potential implications and future directions for this rapidly evolving field.

DOI: 10.5281/zenodo.22201036

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