Based Quantum State Optimization Algorithms

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22153330

Related papers

Quantum Computing Algorithm Optimization

Jincheng Zhang

This paper presents a novel algorithm optimization method leveraging the unique capabilities of quantum computing. Traditional optimization algorithms often struggle with complex systems, requiring significant computational resources and time. We propose a quantum-enhanced optimization approach designed to overcome these limitations by utilizing quantum parallelism and superposition. The core mechanism involves mapping the optimization problem onto a quantum state representation, enabling parallel processing and enhanced exploration of the solution space. The paper details the design of the quantum algorithm, its potential advantages over classical approaches, and preliminary experimental results demonstrating its effectiveness in specific optimization scenarios.

Sep 6, 20260 citations

基于动态量子态的自适应优化算法

Jincheng Zhang

This paper introduces a novel optimization algorithm based on dynamic quantum states. Traditional optimization methods are often static, limiting their effectiveness in complex and dynamic problems. We propose a new algorithm that leverages the inherent quantum state dynamics to dynamically adjust the optimization target, enabling a more efficient and adaptable optimization process. This approach aims to overcome the limitations of conventional algorithms by introducing a mechanism that continuously refines the solution space through quantum state manipulations. The core of the algorithm lies in the creation and manipulation of quantum states, which are then used to guide the optimization process. The presented method demonstrates improved performance compared to existing optimization techniques, particularly in scenarios with high dimensionality and complex objective functions. This work explores the potential of utilizing quantum phenomena to enhance optimization strategies.

Sep 6, 20260 citations

Quantum Entanglement Algorithm Optimization

Jincheng Zhang

This paper introduces a novel algorithm optimization framework based on quantum entanglement. Traditional algorithm optimization methods rely on human-designed strategies, while this framework leverages the unique properties of quantum entanglement to accelerate computation and enable new algorithmic design. We propose a method for manipulating entangled states to enhance the performance of optimization algorithms, demonstrating significant improvements in efficiency and accuracy across a range of benchmark problems. The core mechanism involves the creation and utilization of entangled states to guide the search process and facilitate rapid convergence. We present a detailed theoretical analysis and experimental validation of the proposed algorithm, highlighting its potential to revolutionize the field of algorithm optimization.

Sep 5, 20260 citations

Core Claim: Design a quantum-adaptive optimization algorithm to solve complex optimization problems, leveraging quantum properties to enhance algorithm efficiency.

Jincheng Zhang

This paper presents a novel quantum-adaptive optimization algorithm designed to address challenges in computationally intensive optimization tasks. The core concept revolves around employing quantum mechanics to simulate the optimization process, allowing for a fundamentally different approach compared to conventional optimization methods. We introduce a quantum-driven simulation framework that utilizes quantum properties to accelerate the algorithm's execution and potentially discover novel solutions. This work explores the potential of quantum computing to improve the performance of optimization problems across various domains, offering a promising alternative for scenarios where classical methods struggle. The research focuses on a specific, yet impactful, application: optimizing a complex, multi-dimensional function with intricate constraints. The algorithm's design incorporates key elements such as quantum state manipulation and entanglement to achieve improved convergence and solution quality. This paper details the theoretical underpinnings, implementation details, and preliminary experimental results demonstrating the algorithm's effectiveness. We will also discuss the challenges and future directions for this research, including exploring the scalability of the quantum-adaptive approach.

Aug 31, 20260 citations

基于量子隐冲突的全局优化算法

Jincheng Zhang

This paper presents a novel approach to global optimization utilizing a quantum entanglement-based algorithm. Traditional optimization techniques often struggle with the impact of local optima, potentially hindering the search for the global optimum. We introduce a quantum entanglement-driven algorithm that leverages the unique properties of quantum entanglement to enhance the search process, reducing computational complexity and improving robustness. The core mechanism involves strategically manipulating entanglement to guide the search towards promising regions of the solution space. This paper details the algorithm's design, implementation, and preliminary results, demonstrating its potential for significantly improved performance in complex optimization problems. The core claim is that this quantum entanglement-based algorithm offers a substantial improvement over existing methods in terms of accuracy and computational efficiency.

Sep 7, 20260 citations

Quantum-Based Probabilistic Optimization Algorithm

Jincheng Zhang

This paper introduces a novel probabilistic optimization algorithm leveraging the unique capabilities of quantum computing. Traditional optimization techniques often struggle with large-scale problems, limiting their applicability. We propose a quantum-enhanced algorithm designed to accelerate and improve the solution of complex optimization tasks. This algorithm utilizes quantum mechanics principles to enhance the search process, leading to more efficient and accurate results. The core mechanism centers around quantum superposition and entanglement to explore solution spaces in a fundamentally different manner. We detail the algorithm's design, the quantum circuit implementation, and the resulting performance improvements compared to classical approaches. The paper concludes with a discussion of the potential implications of this approach for various real-world optimization challenges.

Sep 6, 20260 citations