基于自适应的量子纠缠优化算法

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum computing holds immense promise for revolutionizing various fields, and the development of efficient quantum algorithms is crucial for realizing this potential. Current quantum algorithms often struggle with the inherent complexities of quantum systems, leading to suboptimal performance. This paper proposes a novel quantum entanglement optimization algorithm based on adaptive parameter adjustment, designed to address these challenges. We present a theoretical framework outlining the algorithm's core mechanism and discuss the potential benefits of this approach in enhancing quantum computation's efficiency and accuracy. This work contributes to the ongoing effort to improve the design and implementation of quantum algorithms, ultimately accelerating the advancement of quantum technology.

DOI: 10.5281/zenodo.22511757

Related papers

基于自适应的量子计算的拓扑优化

Jincheng Zhang

Quantum computing, leveraging the bizarre properties of quantum mechanics, holds immense potential for revolutionizing various fields. However, achieving practical quantum computation necessitates overcoming fundamental challenges, particularly in algorithm design and optimization. This paper explores the application of a novel self-adaptive quantum optimization algorithm, inspired by the principles of quantum topology and entanglement. We introduce a system that dynamically adjusts quantum parameters to enhance computational efficiency and explore novel solutions to complex problems. The core mechanism centers around leveraging quantum entanglement to guide the optimization process, ultimately leading to improved performance. The proposed algorithm offers a promising approach to addressing the limitations of current quantum computing techniques and opens new avenues for scientific discovery and technological advancement.

Sep 4, 20260 citations

拓扑张效性量子计算的自适应优化算法

Jincheng Zhang

Quantum computing is poised to revolutionize various fields, offering unprecedented computational power. However, realizing this potential necessitates overcoming fundamental challenges, particularly in managing and optimizing quantum states, especially entangled states. This paper investigates a novel self-adaptive optimization algorithm for quantum state correction, designed to enhance the computational capabilities of quantum systems and improve stability. The algorithm leverages the inherent adaptability of quantum entanglement to dynamically adjust the fidelity of these entangled states, leading to a significant improvement in efficiency and resilience. We introduce a framework that dynamically adjusts the entanglement parameters based on the evolution of the quantum system, offering a distinct approach to optimizing quantum computation. The core mechanism focuses on refining the entanglement topology through iterative adjustments, ultimately addressing a critical bottleneck in current quantum algorithms. The potential impact of this algorithm is substantial, offering a pathway towards more robust and efficient quantum computations across diverse applications.

Sep 4, 20260 citations

自适应拓扑量子计算的量子算法优化

Jincheng Zhang

Quantum computing holds immense promise for revolutionizing various fields, and the development of efficient quantum algorithms is crucial for realizing this potential. However, the inherent complexity of quantum systems often necessitates sophisticated optimization techniques. This paper introduces a novel approach to quantum algorithm optimization, centered around the dynamic adjustment of quantum algorithm parameters through the exploitation of quantum topology. We propose a framework that dynamically adjusts these parameters based on the system's state, resulting in enhanced algorithm efficiency and accuracy. This method offers a fundamentally new perspective on quantum algorithm optimization, leveraging the unique properties of quantum topology to guide the optimization process. The core mechanism involves manipulating the quantum state to achieve optimal performance. This work presents a preliminary investigation of this approach and outlines potential future research directions.

Sep 4, 20260 citations

基于自适应自律的量子计算算法优化

Jincheng Zhang

Quantum computing holds immense promise for revolutionizing various fields, and efficient algorithm design is crucial for realizing this potential. However, current quantum algorithms often struggle with optimal parameter settings, limiting their performance. This paper introduces a novel quantum computing algorithm optimization method, leveraging adaptive self-regulation to dynamically adjust quantum computation parameters based on the specific characteristics of the quantum system. We demonstrate the effectiveness of this approach through a series of simulations and analysis, showcasing a significant improvement in algorithm efficiency and precision compared to traditional optimization techniques. The core mechanism focuses on establishing a self-regulating feedback loop that adapts the quantum parameters in real-time, enhancing the algorithm's ability to navigate complex quantum states. This approach offers a more flexible and powerful method for optimizing quantum algorithms, paving the way for enhanced computational capabilities.

Aug 31, 20260 citations

自适应拓扑的量子算法加速

Jincheng Zhang

Quantum computing holds immense promise for tackling complex problems currently intractable for classical computers. However, the development and implementation of quantum algorithms are often limited by the inherent computational complexity of these algorithms. This paper investigates a novel quantum algorithm acceleration technique based on adaptive topology, leveraging the unique properties of quantum entanglement to significantly enhance the performance of various quantum algorithms. We propose a framework wherein the algorithm dynamically adjusts its quantum state to optimally exploit entanglement, thereby dramatically reducing computational time and increasing the likelihood of achieving desired outcomes. Our work demonstrates the efficacy of this approach through detailed theoretical analysis and successful implementation of several quantum algorithms, highlighting its potential to revolutionize quantum computing.

Sep 4, 20260 citations

自适应量子算法的优化策略与硬件设计

Jincheng Zhang

Quantum computing holds immense potential for scientific discovery and technological advancement, but current limitations in algorithm design and hardware implementation restrict its widespread adoption. This paper investigates a novel optimization strategy for quantum algorithms, leveraging the inherent properties of quantum hardware. We propose a dynamically adaptive algorithm that adjusts quantum gate parameters and entanglement strength based on real-time hardware feedback. The core mechanism centers around a "quantum system state" optimization algorithm, meticulously calibrated to the specific characteristics of the target quantum device. This approach aims to significantly improve algorithm efficiency and enhance quantum hardware performance, offering a pathway towards practical quantum applications.

Sep 6, 20260 citations