基于量子计算的复杂变量模型优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel quantum computing-based optimization algorithm designed to address the limitations of traditional optimization methods when applied to complex systems. The algorithm leverages quantum phenomena, specifically quantum superposition and entanglement, to efficiently explore the solution space of complex variable models. We demonstrate the effectiveness of this approach through a series of numerical experiments and comparisons against established optimization techniques. The core mechanism relies on constructing a quantum variable model that can represent the system's dynamics and iteratively refine parameters to achieve optimal performance. The research provides a significant advancement in optimization, offering the potential to drastically improve the performance of complex systems across various domains.

DOI: 10.5281/zenodo.22192929

Related papers

基于量子隐形矩阵的复杂系统优化算法

Jincheng Zhang

This paper introduces a novel optimization algorithm based on quantum entanglement principles to address complex system optimization problems. The algorithm leverages quantum entanglement to enhance the efficiency of solving intricate optimization tasks, particularly those involving multiple variables and constraints. We demonstrate its effectiveness through a series of rigorous simulations and analyses, highlighting its potential to significantly outperform traditional optimization methods in various real-world scenarios. The core mechanism centers around introducing quantum entanglement during the optimization process, enabling the algorithm to explore a wider solution space and effectively navigate complex landscapes. This work represents a significant advancement in optimization techniques, holding promise for applications across diverse fields.

Aug 31, 20260 citations

Quantum-Inspired Optimization for Complex Systems

Jincheng Zhang

Quantum-inspired optimization aims to improve the performance of complex optimization problems by incorporating principles from quantum mechanics. This paper explores the application of quantum-like concepts, such as superposition and entanglement, to enhance the efficiency of optimization algorithms. We present a novel quantum-inspired optimization framework designed to address the challenges inherent in complex systems, offering a fundamentally different approach compared to traditional optimization methods. The core mechanism focuses on leveraging these quantum principles to achieve improved solution paths and faster convergence. This research contributes to the advancement of optimization techniques with the potential for significant gains in the solution of challenging problems across diverse fields.

Sep 5, 20260 citations

Core Claim: A Quantum-Adaptive Complex Model Optimization Method utilizing Quantum Computing for Enhanced Model Performance

Jincheng Zhang

This paper presents a novel quantum-adaptive complex model optimization method, leveraging the unique properties of quantum computing to accelerate and improve the optimization process. The core concept revolves around employing quantum algorithms to achieve computationally more efficient and effective model refinement, ultimately leading to superior performance across various machine learning tasks. We introduce a framework that dynamically adjusts optimization strategies based on quantum-enhanced feature representation, addressing limitations of traditional optimization techniques. This approach aims to provide a significant advancement in model optimization, particularly for complex, high-dimensional datasets. The research focuses on demonstrating the efficacy of this method through rigorous theoretical analysis and preliminary experiments across a range of benchmark problems. The paper concludes with a discussion of the potential impact of this technology on various machine learning domains.

Sep 5, 20260 citations

基于量子网络的参数调整算法

Jincheng Zhang

Quantum computing harnesses the principles of quantum mechanics to perform computations that are intractable for classical computers. However, quantum systems are notoriously sensitive to environmental noise, requiring precise and complex parameter adjustments to achieve optimal performance. Traditional parameter adjustment methods often involve lengthy, iterative processes and can be limited in their ability to effectively explore the parameter space. This paper proposes a novel algorithm based on quantum networks, leveraging quantum entanglement and superposition to dynamically optimize quantum network parameters, ultimately enhancing the efficiency and performance of quantum computations. This research focuses on a systematic approach to parameter adjustment, driven by the unique properties of these quantum phenomena. The core mechanism involves constructing a quantum network and employing quantum algorithms to iteratively adjust parameters based on the network's state evolution. We present a theoretical framework and a preliminary implementation demonstrating the algorithm's potential for improved quantum computation.

Sep 2, 20260 citations

基于量子纠缠的动态时空优化算法

Jincheng Zhang

This paper presents a novel dynamic time-space optimization algorithm based on quantum entanglement principles. Traditional optimization algorithms often rely on static parameter adjustments, limiting efficiency and generalization. We introduce a quantum state representation and a dynamically-adjusted optimization process that leverages the inherent properties of quantum entanglement to optimize solutions effectively. The algorithm's core mechanism involves creating and manipulating entangled quantum states to represent and modify the optimization landscape, resulting in improved performance across a range of problem instances. This work explores the potential of quantum mechanics to revolutionize optimization methodologies, offering a fundamentally different approach compared to conventional techniques. The proposed algorithm demonstrates promising results on several benchmark problems, showcasing its capability to navigate complex optimization landscapes with enhanced adaptability.

Sep 7, 20260 citations

Based Quantum State Optimization Algorithms

Jincheng Zhang

This paper presents a novel approach to optimization utilizing quantum state manipulation. Traditional optimization algorithms often struggle with high-dimensional problems due to computational limitations. This work proposes a new algorithm, termed "Quantum-Guided Optimization," which leverages quantum state properties – specifically superposition and entanglement – to achieve significant efficiency gains in complex optimization scenarios. The core mechanism centers around a quantum state transformation process, which iteratively evolves the search space, effectively guiding the algorithm towards optimal solutions. The paper details the algorithm's design, mathematical foundations, and preliminary experimental results demonstrating improved performance compared to existing classical optimization techniques across a range of benchmark problems. The investigation focuses on the potential of quantum state manipulation to overcome inherent computational bottlenecks in optimization.

Aug 29, 20260 citations