基于量子拓扑的量子计算的自适应拓扑调整

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum computing offers unprecedented computational power, but current challenges lie in achieving reliable and precise results, particularly in complex calculations. This paper investigates a novel approach to quantum computation utilizing the principles of quantum topology – specifically, a dynamically adjustable topology shift. We propose a method based on quantum system evolution and dynamic adjustment of quantum topology, designed to enhance the accuracy of quantum computations by adapting the underlying structure during the computation. The core mechanism leverages the inherent properties of quantum topology to dynamically tailor the system's state, thereby enabling a more precise and efficient solution. This research explores the potential of such a technique to overcome limitations of traditional quantum algorithms, offering a promising pathway towards advancing the field of quantum computing.

DOI: 10.5281/zenodo.22581911

Related papers

Core Claim: Exploring adaptive topology expansion for quantum computing, leveraging dynamic structural parameter adjustments to achieve quantum computation expansion and complexity.

Jincheng Zhang

Quantum computing offers a paradigm shift in computation, promising unprecedented computational power and solutions to complex problems currently intractable for classical computers. However, current quantum computing architectures are limited by the inherent fragility of qubit states and the difficulty of scaling up quantum systems. This paper investigates adaptive topology expansion, a novel approach to quantum computing that dynamically adjusts the topology of quantum systems to enhance their computational complexity and resilience. We propose a framework based on a system of adjustable parameters that governs the evolution of the quantum state. This system allows for a continuous exploration of quantum states, enabling the efficient mapping of complex problems onto quantum hardware. The core mechanism involves a self-organizing process driven by the interplay of these parameters, creating a robust and adaptable quantum system. Our investigation demonstrates the effectiveness of this approach in achieving significant improvements in quantum entanglement and state fidelity compared to traditional methods. This work presents a fundamental foundation for future advancements in quantum computing, highlighting the potential for dynamically sculpted quantum systems. The significance of this research lies in its ability to overcome limitations inherent in current quantum computing designs, paving the way for more versatile and powerful quantum algorithms.

Sep 2, 20260 citations

自适应拓扑的量子计算的参数调整算法

Jincheng Zhang

This paper presents a novel algorithm for parameter adjustment in quantum computation, leveraging the principles of adaptive topology. Traditional parameter adjustment methods often rely on fixed algorithms, struggling to effectively handle complex environments. We propose a dynamic adjustment approach that utilizes the self-assembling process of quantum systems, dynamically modifying topological control parameters to optimize quantum computation performance and efficiency. The core mechanism involves a sophisticated optimization process guided by the inherent properties of the quantum system, ultimately leading to enhanced computational capabilities. This research explores the potential of this adaptive approach to overcome the limitations of conventional parameter tuning and advance the field of quantum computing.

Sep 7, 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

基于动态拓扑的量子计算的量子拓扑优化

Jincheng Zhang

Quantum computation, leveraging the principles of quantum mechanics, offers the potential for unprecedented computational power. However, the design and optimization of quantum algorithms often involve complex quantum states, demanding sophisticated techniques. This paper introduces a novel approach to quantum computation – a framework based on dynamic topology – that dynamically adjusts quantum state parameters to optimize performance. We explore the application of this technique to quantum topology optimization, demonstrating its potential to enhance the efficiency of quantum algorithms. The core mechanism revolves around leveraging the inherent flexibility of dynamic topology to guide the evolution of quantum states, ultimately leading to improved computational outcomes. This research aims to provide a robust and adaptable method for quantum computation, addressing a critical challenge in the field.

Aug 31, 20260 citations

基于自适应量子计算的拓扑优化算法

Jincheng Zhang

This paper introduces a novel quantum optimization algorithm, termed "Adaptive Topological Optimization," designed to enhance the performance of quantum computations. Traditional optimization methods often struggle with the inherent complexity of quantum systems, necessitating a shift towards quantum-specific approaches. This research focuses on leveraging the unique properties of quantum mechanics – particularly the topological nature of quantum states – to guide optimization processes, offering a potential paradigm shift in quantum algorithm design. The core of this algorithm utilizes a dynamically adjusted quantum state to explore the solution space more efficiently and identify optimal solutions with superior performance. We demonstrate the effectiveness of this approach through a series of simulations and preliminary results, highlighting its potential to significantly improve the efficiency and scalability of quantum computations. The paper concludes with a discussion of the algorithm's underlying principles, its potential applications, and future research directions.

Sep 2, 20260 citations

基于非线性拓扑的量子计算的拓扑优化算法

Jincheng Zhang

Quantum computing offers unprecedented computational power, but the design and optimization of quantum circuits often face significant challenges, particularly in the context of complex quantum algorithms. This paper proposes a novel approach to quantum computing, utilizing a modified version of the topological optimization algorithm, to address these challenges. We introduce a new framework that leverages the inherent topological properties of quantum systems to dynamically adjust the structure of quantum circuits, enabling more efficient and robust optimization. The core mechanism focuses on exploiting the non-linear nature of topological spaces to guide the evolution of quantum states, ultimately improving the performance of quantum algorithms. This research explores the potential of this approach to tackle specific problems in quantum information processing and highlights its advantages over existing methods.

Sep 4, 20260 citations