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##拓扑映射的自适应量子纠缠生成

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

Quantum entanglement, a cornerstone of quantum information, is crucial for secure communication and powerful quantum computation. Traditional methods of generating entanglement often rely on complex, pre-defined protocols. This paper proposes a novel quantum entanglement generation algorithm based on the robust and adaptable properties of topological mappings. We develop a system that dynamically adjusts the mapping parameters to generate entangled states tailored to the evolving quantum state. The core mechanism leverages the inherent topological structure of mappings to create entanglement without requiring explicit manipulation of the underlying quantum states. This approach offers a promising pathway towards efficient and adaptable quantum entanglement generation, potentially revolutionizing quantum communication and computation. We detail the algorithm's architecture, key mathematical formulations, and preliminary experimental results demonstrating its effectiveness.

DOI
10.5281/zenodo.22489825

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This paper introduces a novel dynamic topological mapping algorithm leveraging the inherent properties of quantum entanglement. Traditional topological mapping algorithms often rely on static parameter adjustments, limiting their efficiency and generalization ability. Our proposed algorithm dynamically adjusts mapping parameters based on the quantum state's self-adaptation, resulting in improved performance. We detail the core mechanism, explore its advantages, and present preliminary results demonstrating the effectiveness of this approach.
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This paper presents a novel quantum entanglement learning algorithm leveraging the principles of adaptive topology. Traditional quantum entanglement learning methods often rely on manipulating individual qubits, while this approach aims to dynamically construct and refine a quantum state to optimize entanglement properties. We introduce a framework centered around the construction of a 'topology map' that guides the evolution of the quantum state, allowing for a more efficient and accurate learning process. The core mechanism focuses on exploiting the inherent properties of quantum topology to represent and manipulate entanglement, leading to improved entanglement fidelity and reduced computational complexity. The algorithm demonstrates promising results in achieving precise entanglement manipulation, offering a potential pathway to enhanced quantum information processing.
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