基于量子自旋的动态拓扑算法的自适应参数调整

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel dynamic topology algorithm based on quantum spin dynamics. Traditional optimization algorithms often rely on static parameter settings, limiting their effectiveness. We propose a method that dynamically adjusts the quantum spin parameters, allowing for more efficient parameter tuning and improved algorithm performance. The algorithm leverages the inherent properties of quantum spin to iteratively refine the system's state, leading to enhanced results. The core mechanism involves a self-consistent update loop that modifies the spin states based on a predefined objective function. This approach offers a fundamentally different perspective on parameter optimization, potentially yielding superior outcomes compared to conventional methods. We detail the algorithm's design, theoretical analysis, and preliminary experimental validation, demonstrating its effectiveness in a specific quantum optimization problem.

DOI: 10.5281/zenodo.22465630

Related papers

拓扑量子算法的自适应参数优化

Jincheng Zhang

This paper presents a novel approach to quantum algorithm optimization utilizing the principles of topological quantum algorithms. Traditional optimization methods often rely on fixed parameters, limiting the algorithm's adaptability. We propose a method that dynamically adjusts quantum system parameters based on the system's inherent topology, allowing for automated optimization and enhanced algorithm performance. The core mechanism involves constructing a topological representation of the quantum system and iteratively refining this representation to guide the algorithm towards optimal solutions. This approach offers a significant advancement in the field, addressing the limitations of conventional optimization techniques by leveraging the inherent properties of quantum systems. The work details the design of a robust parameter adjustment strategy and provides a preliminary assessment of its efficacy through theoretical analysis and simulated results.

Sep 2, 20260 citations

基于自适应拓扑的量子计算算法优化

Jincheng Zhang

Quantum computing, leveraging the unique properties of quantum systems, offers the potential for unprecedented computational power. However, the development of efficient quantum algorithms is often hindered by the inherent complexity of quantum systems and the challenges of optimization. This paper introduces a novel quantum algorithm optimization method based on adaptive topology, designed to improve the performance of existing quantum algorithms. The core mechanism utilizes the self-adaptative nature of quantum spin states to dynamically adjust quantum parameters and improve the fidelity of quantum computations. We present a theoretical framework and preliminary experimental results demonstrating the effectiveness of this approach in optimizing a specific quantum circuit. The proposed method offers a promising path towards more efficient and reliable quantum computations. The core claim of the paper is that this adaptive topology-based algorithm significantly enhances the performance of quantum algorithms compared to traditional optimization techniques.

Aug 31, 20260 citations

基于量子自旋的动态拓扑算法评估

Jincheng Zhang

This paper explores the application of quantum spin dynamics to the assessment of topological structures. Traditional methods often rely on static analysis, which can be limited in capturing the intricate changes within a topological system. We propose a novel dynamic quantum spin algorithm that dynamically adjusts quantum spin parameters to evaluate the evolution of topological structures. This approach offers the potential for significantly improved accuracy and efficiency in assessing the robustness and dynamic behavior of topological materials. The core mechanism involves a continuous, iterative adjustment of the quantum spin states, driven by a predefined set of parameters, to mimic the iterative processes involved in topological phase transitions. We demonstrate the effectiveness of this method through simulations and preliminary experiments, showcasing its ability to accurately characterize the stability and responsiveness of various topological systems. The results indicate a substantial improvement in the assessment of topological structure dynamics compared to traditional approaches.

Sep 6, 20260 citations

基于自适应量子拓扑的自适应几何优化算法

Jincheng Zhang

This paper introduces a novel adaptive geometric optimization algorithm based on quantum topology. Traditional geometric optimization methods often rely on fixed parameter settings, limiting their ability to effectively address complex geometric problems. This paper proposes a new algorithm that dynamically adjusts geometric optimization parameters based on the problem's characteristics, enabling more efficient optimization. We leverage the inherent adaptability of quantum topology to achieve this dynamic adjustment. The core mechanism involves utilizing quantum topological properties to iteratively refine geometric parameters, leading to improved solutions compared to conventional methods. We demonstrate the effectiveness of our algorithm through several illustrative examples, showcasing its ability to handle challenging geometric configurations.

Aug 31, 20260 citations

自适应量子拓扑的优化算法

Jincheng Zhang

This paper introduces a novel optimization algorithm based on the principles of quantum topology, designed to dynamically adjust quantum system parameters in response to environmental fluctuations. Traditional optimization methods often rely on predefined algorithms, which limits their adaptability and scalability. This work proposes a framework leveraging the inherent robustness and self-healing properties of quantum topology to achieve efficient quantum computation. The core mechanism involves a self-evolving parameter set that adapts to the surrounding quantum environment, leading to improved performance and robustness. We demonstrate the effectiveness of this algorithm through simulations and analysis, highlighting its potential to overcome limitations of conventional optimization techniques.

Sep 7, 20260 citations

基于量子自旋的拓扑结构优化算法

Jincheng Zhang

This paper introduces a novel quantum spin-based optimization algorithm designed to enhance the performance of quantum computing algorithms. Traditional optimization methods rely on fixed parameters, often limiting their effectiveness in complex problems. Our approach leverages the inherent adaptability of quantum spin systems to dynamically adjust optimization parameters, promising significant improvements in computational efficiency. The core mechanism centers around utilizing the self-steering properties of quantum spin systems to guide the optimization process, resulting in more robust and efficient solutions compared to conventional algorithms. This work demonstrates the potential of this algorithm to address challenges in the design and implementation of quantum algorithms.

Aug 31, 20260 citations