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Core Claim: Design a self-adaptive algorithm based on quantum entanglement, capable of dynamically adjusting algorithm parameters in response to small changes in input data, thereby enhancing algorithm adaptability.

Jincheng Zhang

Published Sep 2, 2026• 0 citations
Publisher

Abstract

Quantum entanglement, a cornerstone of quantum mechanics, offers a potentially transformative approach to algorithm design. This research investigates the implementation of a self-adaptive algorithm leveraging quantum entanglement to enhance algorithmic adaptability. The core concept revolves around utilizing entangled states to dynamically adjust algorithm parameters in response to subtle variations introduced by input data. This approach contrasts with traditional algorithms that utilize fixed parameters, potentially addressing limitations in handling complex and dynamic environments. The study details the design and implementation of this novel algorithm, providing a framework for adaptive algorithm optimization based on quantum entanglement principles. The research explores the potential advantages and challenges associated with this methodology, focusing on its capacity to maintain performance under varying input conditions and demonstrate a significant improvement in algorithm robustness compared to static parameter-based approaches. The core mechanism utilizes quantum entanglement to establish a dynamic relationship between input data and algorithm parameters, fostering a self-correcting and adaptable system. This paper presents a comprehensive analysis of this approach, outlining the theoretical foundation, implementation details, and preliminary experimental results supporting its efficacy. The investigation will assess the algorithm's sensitivity to input fluctuations, its ability to maintain accuracy across a range of data variations, and its overall performance in a simulated environment. The ultimate goal is to establish a robust and adaptable algorithm design strategy leveraging the unique properties of quantum entanglement.

DOI
10.5281/zenodo.22243534

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