Quantum Algorithms for Adaptive Topological Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel quantum algorithm for adaptive topological optimization, leveraging quantum topology to enhance algorithm efficiency and performance. Traditional topological optimization methods often rely on static topology, limiting their adaptability. Our approach introduces a quantum algorithm that dynamically adjusts the topology of the search space during optimization, enabling more efficient and effective quantum computations. We detail the core mechanism, including the quantum state representation and the optimization process, and discuss the potential benefits of this approach. The paper concludes with a summary of the key findings and future research directions.

DOI: 10.5281/zenodo.22509704

Related papers

Quantum Adaptive Topological Optimization Algorithm

Jincheng Zhang

This paper introduces a novel quantum adaptive topology optimization algorithm designed to enhance the performance of quantum computation. Traditional optimization methods often rely on static parameters, limiting the algorithm's adaptability. This research proposes a quantum-enhanced approach that dynamically adjusts the topology of a quantum system based on its internal state, enabling more efficient and precise solutions. We demonstrate the effectiveness of this algorithm through a series of simulations and analysis, highlighting its potential to overcome limitations of existing quantum optimization techniques. This algorithm leverages the inherent quantum properties to achieve a level of flexibility and robustness not previously achievable.

Sep 3, 20260 citations

The Quantum Topological Adaptive Parameter Optimization Algorithm

Jincheng Zhang

This paper introduces a novel quantum topological adaptive parameter optimization algorithm designed to enhance the performance of quantum computations. Traditional parameter optimization methods are often static, limiting the algorithm's ability to adapt to dynamic system changes. Our algorithm leverages the unique properties of quantum topology to dynamically adjust parameters, enabling a more efficient and accurate optimization process. This innovation addresses the limitations of current approaches and holds significant promise for advancing the field of quantum computing. We detail the core mechanism, the algorithm's operational steps, and the resulting performance improvements achieved through this quantum-enhanced optimization approach.

Aug 31, 20260 citations

Adaptive Quantum State Topology Optimization Algorithm

Jincheng Zhang

This paper introduces an adaptive quantum state topology optimization algorithm designed to enhance quantum computing efficiency by dynamically adjusting quantum state parameters based on the system's evolving topology. Traditional optimization methods often rely on static parameters, limiting their ability to effectively handle dynamic topology. We propose a novel approach that leverages a quantum state's 'topological deformation' process to optimize quantum state parameters, resulting in improved performance. This algorithm utilizes a feedback mechanism to continuously refine the quantum state, addressing a significant limitation of existing approaches. The core mechanism involves a continuous transformation of the quantum state, guided by an optimization criterion, to achieve optimal performance within a defined range of possible states. This paper details the algorithm's design, validation through simulations, and potential future enhancements.

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

Self-Adaptive Topological Transformation Algorithm for Quantum Computing

Jincheng Zhang

This paper introduces a novel self-adaptive topological transformation algorithm designed for efficient execution of quantum computations on quantum computers. Traditional topological transformation algorithms often rely on static transformations, limiting their flexibility and performance. This work proposes a dynamic approach to topology, adjusting the topology structure during computation to optimize quantum circuit performance. The core mechanism involves iteratively refining the topology through a series of adjustments, ultimately achieving improved efficiency and scalability. We detail the algorithm's architecture, mathematical framework, and empirical validation results, demonstrating its potential to significantly enhance quantum computing capabilities. The paper concludes with a discussion of future research directions and potential applications.

Sep 4, 20260 citations

Dynamic Topology Quantum Algorithm Optimization

Jincheng Zhang

This paper explores the application of dynamic topology to quantum algorithm optimization, aiming to enhance both efficiency and accuracy. Traditional quantum algorithm optimization often relies on static approaches, struggling to effectively adapt to dynamic changes. We propose a novel method utilizing dynamic topology to dynamically adjust the quantum algorithm's structure, thereby significantly improving its performance. This involves iteratively modifying the topology of a quantum system to achieve optimal results. The paper details the proposed algorithm, its implementation, and preliminary results demonstrating substantial improvements across a range of quantum algorithms. Furthermore, we discuss the potential benefits and challenges of this approach for future research and development within the field of quantum computing.

Sep 3, 20260 citations