基于自适应学习的量子计算的拓扑密度优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum computing offers the potential for unprecedented computational power, but realizing this potential requires careful management of quantum states. The stability and efficiency of quantum computations are highly dependent on the precise control and optimization of these states. This paper investigates a novel quantum computing optimization algorithm based on adaptive learning, specifically designed to dynamically adjust quantum potential fields to enhance stability and efficiency. We propose a mechanism that iteratively adjusts the quantum potential, responding to changes in the system, to achieve optimal quantum states. The core claim of this work is that the proposed adaptive learning approach can significantly improve the performance of quantum computations compared to traditional optimization methods. This is achieved through a dynamic and responsive quantum potential landscape, allowing for more efficient exploration of the state space. The paper details the algorithm's implementation, demonstrates its effectiveness through simulations, and discusses the potential implications for future quantum computing development.

DOI: 10.5281/zenodo.22264464

Related papers

基于自适应量子势场的优化算法

Jincheng Zhang

The development of efficient quantum algorithms is critically dependent on the ability to precisely control and optimize quantum systems. Traditional approaches to quantum system optimization often rely on static potentials, limiting their adaptability and potential for improvement. This paper introduces a novel optimization algorithm based on dynamically adjusting quantum potentials, leveraging the inherent change in quantum environments to achieve enhanced performance and efficiency. We detail the core mechanism, outlining the iterative process of characterizing, adjusting, and analyzing the quantum potential field to guide the optimization of quantum states. The resulting algorithm demonstrates improved performance compared to existing methods across a range of quantum tasks, offering a significant step forward in the field of quantum computation.

Sep 6, 20260 citations

自适应张量映射 - 动态参数调整的量子计算优化

Jincheng Zhang

This paper introduces a novel adaptive tensor mapping method designed for quantum computing and beyond. Traditional quantum computing optimization often focuses on algorithm design, while this approach dynamically adjusts tensor mappings to precisely control quantum potential fields, enhancing algorithm efficiency and performance. We propose a self-adaptive algorithm that iteratively refines tensor mappings based on the current system state and target calculation, offering a dynamic and potentially more effective solution than static optimization techniques. The core mechanism involves a feedback loop that continuously adjusts parameters to achieve optimal quantum state. This work aims to address limitations of existing quantum computing optimization strategies by integrating dynamic parameter control and a novel adaptive approach. The paper concludes with a discussion of the potential implications and future research directions of this method.

Aug 31, 20260 citations

基于自适应的量子计算的拓扑结构优化

Jincheng Zhang

Quantum computing offers the potential for revolutionary computational capabilities, but realizing this potential requires careful design of quantum circuits and algorithms. Quantum state optimization is a crucial step in this process, and traditional optimization methods often struggle with the inherent complexity of quantum systems. This paper presents a novel approach to quantum state optimization utilizing adaptive quantum state transformations, leveraging the self-adaptability of quantum states to achieve enhanced computational efficiency and precision. We detail a method that dynamically adjusts the quantum state topology to address specific challenges, resulting in improved performance compared to existing static optimization techniques. The core mechanism focuses on exploiting the quantum state's ability to evolve and adapt to changing conditions, thereby enabling the discovery of optimal quantum circuits. This work addresses the limitations of conventional optimization approaches by introducing a dynamic, self-adjusting process that directly interacts with the quantum system's inherent properties. The proposed method provides a promising path toward accelerating quantum algorithm development and enhancing the performance of various quantum computations.

Aug 29, 20260 citations

基于自适应的量子态的超导量子计算算法

Jincheng Zhang

This paper presents a novel approach to quantum computing, leveraging adaptive quantum state parameter adjustment to enhance the stability and efficiency of quantum computations. Traditional quantum algorithms often rely on static parameter settings, limiting their potential for optimization. We propose a dynamic adjustment mechanism that allows the quantum state to evolve in real-time, optimizing quantum operations and mitigating the effects of noise. The core of our algorithm is based on a carefully designed feedback loop that adjusts parameters based on the observed quantum state. This dynamic adaptation significantly improves the resilience of quantum circuits, potentially enabling the realization of complex quantum algorithms. The implementation demonstrates the effectiveness of this approach in a simulated environment, showcasing a marked improvement in coherence and fidelity compared to existing static parameter control methods. The potential implications for quantum computing are significant, offering a pathway towards more robust and scalable quantum systems.

Aug 31, 20260 citations

自适应量子势场优化算法

Jincheng Zhang

Quantum computing offers tremendous potential, but realizing this potential requires careful design of quantum systems, particularly their quantum states. Current approaches to quantum state design often rely on static optimization strategies, which struggle to effectively address the inherent complexities of quantum systems. This paper introduces a novel self-adaptive quantum field optimization algorithm, designed to dynamically adjust quantum field parameters to enhance quantum computation performance. We propose a mechanism that continuously refines the quantum field based on the system's dynamics, achieving a more tailored and effective optimization process. The core claim of this research is that this algorithm can significantly improve the performance of quantum computations by optimizing quantum field parameters through a self-adaptive process. The paper details the algorithm's design, validation through simulations, and potential future directions.

Sep 4, 20260 citations

Adaptive Quantum Potential Optimization Algorithm

Jincheng Zhang

This paper introduces an Adaptive Quantum Potential Optimization Algorithm (AQPO) designed to enhance the efficiency and stability of quantum computations. Traditional quantum optimization methods often rely on fixed parameters, limiting the algorithm's adaptability and potential for improvement. AQPO leverages a dynamic quantum potential landscape, adjusting the potential energy fields during optimization, resulting in a more robust and efficient process. We present a novel framework incorporating a feedback loop that continuously monitors and adapts the potential, leading to significantly improved performance in various quantum algorithms. The paper details the algorithm's core mechanism, explores its implementation and validation through simulations, and discusses the advantages over existing optimization techniques. This research explores the potential of dynamically adjusting quantum potential to improve the performance of quantum computations.

Sep 6, 20260 citations