Title: Adaptive Quantum Algorithm Optimization Based on Topological Structure

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the application of adaptive quantum algorithm optimization to complex systems, leveraging the unique properties of quantum algorithms to achieve precise optimization results. The core concept revolves around designing an optimization algorithm intrinsically linked to topological structures, utilizing quantum computational techniques to address challenges inherent in traditional optimization methodologies. The research focuses on demonstrating the efficacy of this approach through a rigorous examination of its performance on a selected, albeit simplified, system. The paper will detail the underlying principles of the adaptive optimization strategy, outline the key mathematical frameworks employed, and present preliminary results demonstrating improved accuracy compared to conventional optimization methods. Furthermore, the paper will discuss the potential advantages and limitations of this new approach, emphasizing its suitability for tackling complex, non-linear optimization problems.

DOI: 10.5281/zenodo.22184802

Related papers

Title: Adaptive Complexity Optimization of Quantum Algorithms

Jincheng Zhang

Quantum algorithms, renowned for their potential to solve complex problems beyond the capabilities of classical computers, often face challenges related to computational complexity. This paper introduces a novel approach to address this challenge – Adaptive Complexity Optimization of Quantum Algorithms (ACOA). We propose a method that dynamically adjusts the complexity of quantum algorithms based on the characteristics of the input data. The core mechanism centers around employing a topological quantum algorithm to decompose the algorithm's complexity into modular components, and automatically adjusting these modules based on the input's inherent structure. This adaptive approach aims to improve efficiency and scalability by effectively modeling complex data. The paper details the design of the OACO algorithm, its implementation, and preliminary experimental results demonstrating its effectiveness in tackling specific problem instances. We highlight the advantages of this method over traditional complexity optimization techniques, emphasizing its dynamic adaptability and potential for enhanced performance.

Sep 2, 20260 citations

Title: Adaptive Topological Transformation Quantum Computing Model (QATQ)

Jincheng Zhang

This paper introduces a novel quantum computing model, QATQ, based on adaptive topological transformation techniques. We propose a framework that dynamically adjusts the quantum state evolution to simulate system dynamics and leverages topological structures for optimization. The core mechanism focuses on combining quantum algorithms with adaptive topological transformations to improve efficiency and address the challenges of traditional quantum algorithms. This research explores the potential of QATQ to enhance the performance of various quantum computations, offering a fundamentally different approach to quantum system modeling. The paper details the methodology, provides a theoretical analysis, and presents preliminary experimental results demonstrating the efficacy of the QATQ approach. The study aims to advance the state of quantum computing by introducing a dynamic and adaptive method.

Sep 7, 20260 citations

Title: Adaptive Quantum Topology Mapping Optimization

Jincheng Zhang

Quantum topology, a fundamentally different approach to quantum physics, offers the potential for novel computational paradigms. This paper explores the application of adaptive quantum topology mapping optimization to enhance the performance of quantum computers. We propose a framework that dynamically adjusts the underlying topology of a quantum system, enabling automated refinement and optimization of quantum states. This is driven by the inherent adaptability of quantum topology, allowing for the exploration of complex quantum states with enhanced precision. The core mechanism involves employing a self-organizing algorithm to iteratively reshape the topology of the system, resulting in improved computational accuracy and efficiency. The study demonstrates the potential of this approach to address challenges in quantum algorithm design and optimization.

Aug 31, 20260 citations

Title: Adaptive Quantum State Optimization Algorithm

Jincheng Zhang

This paper introduces an adaptive optimization algorithm based on quantum state principles, leveraging quantum state superposition to model and solve complex systems effectively. The core mechanism centers around utilizing quantum state manipulation to accelerate optimization processes. This approach addresses existing algorithmic limitations by exploiting quantum properties, promising improved efficiency and scalability. The proposed algorithm seeks to overcome computational bottlenecks inherent in traditional optimization methods through the application of quantum entanglement. This research investigates the potential of quantum state representations for enhanced optimization capabilities, offering a novel methodology for tackling challenging optimization problems.

Sep 7, 20260 citations

Core Claim: Design a self-adaptive algorithm based on quantum entanglement, capable of dynamically adjusting algorithm parameters in response to small changes in input data, thereby enhancing algorithm adaptability.

Jincheng Zhang

Quantum entanglement, a cornerstone of quantum mechanics, offers a potentially transformative approach to algorithm design. This research investigates the implementation of a self-adaptive algorithm leveraging quantum entanglement to enhance algorithmic adaptability. The core concept revolves around utilizing entangled states to dynamically adjust algorithm parameters in response to subtle variations introduced by input data. This approach contrasts with traditional algorithms that utilize fixed parameters, potentially addressing limitations in handling complex and dynamic environments. The study details the design and implementation of this novel algorithm, providing a framework for adaptive algorithm optimization based on quantum entanglement principles. The research explores the potential advantages and challenges associated with this methodology, focusing on its capacity to maintain performance under varying input conditions and demonstrate a significant improvement in algorithm robustness compared to static parameter-based approaches. The core mechanism utilizes quantum entanglement to establish a dynamic relationship between input data and algorithm parameters, fostering a self-correcting and adaptable system. This paper presents a comprehensive analysis of this approach, outlining the theoretical foundation, implementation details, and preliminary experimental results supporting its efficacy. The investigation will assess the algorithm's sensitivity to input fluctuations, its ability to maintain accuracy across a range of data variations, and its overall performance in a simulated environment. The ultimate goal is to establish a robust and adaptable algorithm design strategy leveraging the unique properties of quantum entanglement.

Sep 2, 20260 citations

Title: Adaptive Quantum Algorithm-Based Topological Optimization Model

Jincheng Zhang

This paper introduces a novel topological optimization model utilizing an adaptive quantum algorithm. Traditional optimization methods rely heavily on manual parameter adjustments, while quantum algorithms offer the potential for more precise and efficient optimization. This work proposes a dynamic adjustment of quantum algorithm parameters to enhance optimization performance and improve the accuracy of resulting solutions. The core mechanism centers on leveraging the inherent capabilities of quantum algorithms to dynamically adjust the optimization process, leading to improved results compared to classical optimization techniques. This research investigates the potential of this adaptive approach to solve complex topological optimization problems.

Aug 31, 20260 citations