基于量子势场的自适应优化算法

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel algorithm based on quantum fields, designed for adaptive optimization of parameters within complex systems. We propose a framework that utilizes quantum entanglement to facilitate global optimization, mitigating the pitfalls of traditional optimization techniques. The algorithm leverages a dynamically evolving quantum field, providing a means to explore a wider solution space and avoid local optima. This approach offers the potential for significant improvements in parameter tuning and overall system performance. The core mechanism centers around utilizing entanglement to guide the search process, leading to enhanced efficiency and robustness.

DOI: 10.5281/zenodo.22154008

Related papers

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

基于量子势场的自适应拓扑优化算法

Jincheng Zhang

This paper introduces a novel algorithm for parameter adjustment based on quantum fields, leveraging quantum entanglement to achieve more efficient optimization. Traditional optimization algorithms often struggle with complex problems due to their fixed parameter settings. This proposed approach utilizes a dynamic quantum field representation and quantum entanglement to bypass local optima and potentially unlock significant improvements in parameter tuning across a wide range of problems. The algorithm's core mechanism centers on encoding the problem's parameters within the quantum field, enabling a more sophisticated exploration of the parameter space. We demonstrate its effectiveness through a series of illustrative examples, showcasing enhanced performance and a novel approach to parameter optimization.

Aug 29, 20260 citations

Core Claim: Design a global optimization algorithm based on quantum entanglement, capable of efficiently solving complex optimization problems and achieving global optima.

Jincheng Zhang

Quantum optimization is a rapidly growing field with the potential to revolutionize various scientific and engineering disciplines. Traditional optimization algorithms often struggle to find truly optimal solutions, frequently getting trapped in local optima. Quantum optimization, however, offers a fundamentally different approach utilizing quantum entanglement to explore the solution space in a more holistic manner. This paper presents a novel global optimization algorithm based on quantum entanglement, designed to overcome the limitations of classical optimization methods. We introduce a mechanism that leverages entanglement to facilitate the exploration of a wider solution space, ultimately leading to the discovery of global optima. This work demonstrates the potential of quantum entanglement to significantly improve the efficiency and accuracy of complex optimization problems across diverse domains. The core of this algorithm lies in its intricate use of entangled states to represent and manipulate the search space, enabling the identification of optimal solutions with significantly reduced computational cost. We rigorously analyze the algorithm's performance through extensive simulations and experiments, highlighting its effectiveness in tackling challenging optimization scenarios. This paper provides a detailed exploration of the underlying principles and practical implementation of this groundbreaking approach, paving the way for future advancements in quantum optimization techniques.

Sep 5, 20260 citations

Dimension Space Quantum Entanglement Adaptive Optimization Algorithm

Jincheng Zhang

This paper presents a novel quantum entanglement-based adaptive optimization algorithm designed to address complex optimization problems. The core concept revolves around exploiting the instantaneous information transfer capabilities of entangled states to guide the optimization process, thereby mitigating the pitfalls of local optima and enabling global optimization. The algorithm incorporates a dynamic parameter adjustment mechanism predicated on entanglement, allowing it to adapt to environmental changes and potentially achieve superior performance compared to traditional optimization techniques. This work explores the theoretical foundations of entanglement-based optimization and demonstrates its potential for significant advancements in the field of adaptive learning and problem-solving.

Sep 7, 20260 citations

基于量子计算的自适应优化算法

Jincheng Zhang

This paper introduces a novel quantum computing-based optimization algorithm designed for enhanced performance and generalization capabilities. Traditional optimization algorithms often struggle with complex, high-dimensional problems, requiring extensive parameter tuning. We propose a quantum algorithm that leverages quantum entanglement to dynamically adjust optimization parameters during the optimization process. This dynamic adjustment fosters a more efficient and robust optimization pathway, potentially surpassing the capabilities of classical algorithms in certain scenarios. The core mechanism relies on exploiting quantum properties to explore the solution space more effectively. We analyze the algorithm's potential benefits, including improved convergence speed and robustness against local optima, and provide a preliminary assessment of its applicability to real-world problems. The paper concludes with a discussion of future research directions and potential challenges.

Sep 6, 20260 citations

Quantum Entanglement Adaptive Algorithm for Complex System Optimization

Jincheng Zhang

Quantum entanglement presents a unique opportunity for novel optimization algorithms. This paper explores the design of a quantum entanglement-based adaptive algorithm for complex system optimization. We leverage the inherent properties of entanglement to enable global search and adaptive behavior, resulting in enhanced efficiency. The core mechanism centers around utilizing entangled states to guide the search process, enabling a fundamentally different approach to optimization compared to classical algorithms. We present a theoretical framework and preliminary experimental results demonstrating the potential of this algorithm for complex systems.

Sep 7, 20260 citations