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Dynamic Topology Quantum Adaptative Algorithm

Jincheng Zhang

Published Sep 1, 2026• 0 citations
Publisher

Abstract

This paper investigates the application of dynamic topology principles to quantum computing, specifically focusing on the design of an adaptive quantum algorithm. Traditional quantum algorithms often rely on static calculations, presenting limitations in scalability and efficiency. This research proposes a novel approach utilizing dynamic topology – a mathematical framework that allows for continuous structural changes – to enhance quantum computation. The core mechanism involves constructing a dynamic topology structure that adapts to the quantum state, enabling efficient quantum self-adaptation. The paper demonstrates the potential of this approach to significantly improve the performance of quantum algorithms, particularly in scenarios requiring rapid parameter adjustments.

DOI
10.5281/zenodo.22220603

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Quantum computing offers the potential for unprecedented computational power, but current limitations stem from the difficulty of designing and implementing quantum algorithms. This research introduces a novel approach to quantum algorithm design leveraging the principles of topology. We propose a new framework for constructing adaptive quantum algorithms that dynamically adjust to the specific properties of input quantum states, enabling rapid adaptation to various problems. This addresses a crucial bottleneck in quantum algorithm development, potentially unlocking significant advancements in quantum computing efficiency. The core mechanism involves constructing a topological structure that governs the evolution of quantum states during the algorithm's execution. This structure allows for the algorithm to self-organize and optimize its performance based on the input data, leading to improved results and increased adaptability. This paper details the design of this adaptive algorithm, its theoretical foundation, and preliminary experimental results demonstrating its effectiveness. The resulting algorithms demonstrate a significant improvement in performance compared to traditional quantum algorithms across a range of benchmark problems.
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