基于动态拓扑的量子算法优化器

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel quantum algorithm optimization framework based on dynamic topology. Traditional quantum algorithm optimization often relies on static approaches, limiting potential performance gains. This work proposes a dynamic topology optimization algorithm that adapts the quantum algorithm's structure in real-time to enhance efficiency and precision. We demonstrate the efficacy of this approach through a series of experiments, showcasing significant improvements in quantum circuit performance compared to conventional methods. This innovation represents a significant advancement in quantum algorithm optimization, offering a pathway towards more robust and adaptable quantum solutions.

DOI: 10.5281/zenodo.22157680

Related papers

基于自适应拓扑的量子计算的优化

Jincheng Zhang

This paper introduces a novel quantum computing optimization framework based on adaptive topology. Traditional quantum computing optimization methods typically rely on static parameters, while adaptive topology optimization offers the ability to dynamically adjust parameters for improved resource utilization. Our approach utilizes a dynamic structure generated by the optimization process to enhance quantum computation efficiency. The core mechanism involves adjusting quantum bit (qubit) parameters based on the observed results of a quantum computation, promoting a more efficient and robust approach to optimization. We demonstrate the effectiveness of this framework through simulations and preliminary experiments, showcasing significant improvements in quantum algorithm performance compared to conventional methods.

Sep 3, 20260 citations

基于动态拓扑的量子算法优化框架

Jincheng Zhang

This paper presents a novel quantum algorithm optimization framework based on dynamic topology. Traditional quantum algorithm optimization often relies on static approaches, limiting the potential for significant performance improvements. We introduce a dynamic topology optimization algorithm that dynamically adjusts the quantum algorithm's topology during the optimization process. This adaptation allows the algorithm to exploit inherent quantum properties, leading to enhanced efficiency and precision. The core mechanism involves modifying the quantum gate structure and parameters, leveraging the inherent quantum properties to guide the search space and achieve optimal solutions. The proposed framework demonstrates significant improvements in several benchmark quantum algorithms, highlighting its potential to revolutionize quantum algorithm design.

Aug 29, 20260 citations

Self-Adaptive Quantum Topology Dynamic Optimization Algorithm

Jincheng Zhang

Quantum topology, a novel framework for manipulating quantum states, offers unprecedented opportunities for algorithm design and optimization. This paper presents a self-adaptive quantum topology dynamic optimization algorithm, leveraging the inherent properties of quantum topology to dynamically adjust quantum circuit topologies and enhance algorithm performance. We demonstrate the algorithm's efficacy through a series of rigorous analyses and simulations, showcasing significant improvements in key performance metrics compared to traditional optimization techniques. The core mechanism centers around the continuous exploration and refinement of quantum topology through iterative adjustments, effectively circumventing the limitations of static optimization strategies. This approach provides a promising pathway toward the development of robust and highly adaptable quantum algorithms.

Aug 31, 20260 citations

基于动态拓扑的量子算法验证

Jincheng Zhang

Quantum algorithms are increasingly critical for various applications, demanding robust and adaptable verification methods. Static quantum circuit testing remains a primary approach, but its limitations in handling complex algorithms necessitate novel validation techniques. This paper introduces a novel dynamic topology-based quantum algorithm verification framework, leveraging the inherent flexibility of quantum circuits to dynamically adjust their topology during verification. We propose a method for generating circuit prototypes, executing them with a dynamic topology, and then evaluating the algorithm's performance through a series of topology changes. This framework provides a significant advancement in quantum algorithm validation, enabling real-time optimization and improved performance compared to traditional static methods. The core mechanism focuses on dynamically adjusting circuit topology to explore a wider range of potential solutions.

Aug 31, 20260 citations

基于自适应拓扑的量子系统优化算法

Jincheng Zhang

This paper presents a novel quantum system optimization algorithm based on adaptive topology. Traditional optimization methods often rely on static approaches, offering limited flexibility for complex quantum systems. This work introduces a dynamic adjustment mechanism, leveraging adaptive topology to optimize quantum system parameters and enhance overall performance. The core principle lies in constructing a flexible, self-configuring topology that adapts to the system's state, enabling efficient parameter tuning and improved quantum computation. We demonstrate the effectiveness of this algorithm through simulations and provide a theoretical analysis of its capabilities. The potential impact of this approach on quantum computing is significant, offering a pathway to more efficient and robust quantum systems.

Aug 31, 20260 citations

基于动态自适应的量子计算的自适应几何优化与自适应拓扑优化

Jincheng Zhang

This paper introduces a novel framework for quantum computation based on dynamic adaptive geometry optimization and adaptive topology optimization. Traditional quantum algorithms often rely on static, predetermined geometric structures and topology, which struggle to accommodate dynamic input data. Our proposed method leverages dynamic self-adaptation to automatically adjust the computation's geometry and topology, resulting in enhanced quantum computational efficiency. We demonstrate the effectiveness of this framework through theoretical analysis and experimental validation on a simplified quantum circuit problem. The core mechanism involves iterative refinement of the geometry and topology based on the input data, leading to improved performance compared to static approaches. This work represents a significant advancement in the design of quantum algorithms, specifically tailored for dynamic environments.

Aug 31, 20260 citations