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基于图的量子态计算与优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations
Publisher

Abstract

This paper introduces a novel computational and optimization framework based on graph representations of quantum states. Traditional quantum algorithms often require direct manipulation of the quantum state, which can be computationally expensive and challenging. This work proposes a method that utilizes graph structures to simplify the optimization process inherent in quantum computations, leveraging the topological properties of the graphs to achieve efficiency gains. We present a new algorithm that transforms the optimization problem into a graph-based optimization, enabling a more streamlined and potentially faster approach to quantum state manipulation and optimization. The core mechanism centers around representing the quantum state as a graph, where nodes represent quantum states and edges define relationships and constraints. This graph representation allows for efficient exploration of the solution space and simplifies the optimization process by focusing on the structural properties of the graph rather than the individual quantum states. The potential advantages of this approach include reduced computational complexity, improved scalability, and a more intuitive understanding of the optimization process. We provide a theoretical analysis and initial implementation demonstrating the effectiveness of this method in a specific quantum algorithm.

DOI
10.5281/zenodo.22276868

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This paper introduces a novel algorithm for topological topological computation, leveraging the inherent quantum state structure to accelerate computational processes. Traditional topological computation methods often struggle with complex problems due to computational limitations. This work proposes a new approach that utilizes quantum state manipulation and optimization to overcome these challenges, offering a pathway towards more efficient and scalable topological computations. The core mechanism centers around a dynamic adjustment of quantum states to achieve a more optimal state, facilitating the exploration of topologically rich spaces. We present a preliminary implementation demonstrating the effectiveness of this algorithm in a simplified, yet illustrative, example. The resulting results suggest a significant improvement over existing methods, particularly for certain topological problem domains.
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