Quantum Entanglement Probability Adaptive Algorithm

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum entanglement, a fundamental phenomenon in quantum mechanics, presents unique challenges for classical computation. Traditional optimization methods often rely on meticulously adjusted parameters, failing to fully leverage the inherent probabilistic nature of entangled states. This paper introduces a novel algorithm, the Quantum Entanglement Probability Adaptive Algorithm (QEPAA), designed to dynamically adjust quantum parameters based on the probabilistic fluctuations of entangled states. This approach leverages the concept of "纠缠波动" – a probabilistic variable that governs the evolution of entangled states – to achieve significantly improved computational efficiency, particularly in quantum computing and quantum cryptography. We demonstrate the effectiveness of QEPAA through detailed numerical simulations and analyze its impact on key quantum algorithms. The core mechanism centers around introducing a dynamic weighting system that adapts to the entanglement distribution, leading to optimized results. This algorithmic advancement holds significant potential for overcoming the limitations of current optimization techniques and unlocking new capabilities in quantum technologies.

DOI: 10.5281/zenodo.22221271

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