Title: Quantum-Assisted Dynamic Symmetry Breaking in Optimization

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum-Assisted Dynamic Symmetry Breaking in Optimization addresses the inherent challenges faced by optimization algorithms by introducing a novel approach that leverages quantum principles to detect and break symmetry patterns in problem spaces. This research explores the potential of utilizing quantum phenomena, specifically entanglement and superposition, to identify and disrupt symmetry, ultimately promoting more robust and efficient solutions. We detail a framework for automated symmetry analysis, incorporating quantum-enhanced detection and a dynamic process of symmetry shattering. The resulting system demonstrates a significant improvement in problem-solving capabilities across a range of optimization scenarios, highlighting a fundamentally new paradigm for tackling complex optimization challenges. The proposed method offers a pathway towards algorithms capable of handling non-convex optimization landscapes with greater resilience and speed.

DOI: 10.5281/zenodo.22330844

Related papers

Title: Quantum-Inspired Optimization for Non-Linear Systems

Jincheng Zhang

Quantum-inspired optimization presents a novel approach to solving complex, non-linear optimization problems by incorporating quantum phenomena into the optimization process. This paper investigates the potential of utilizing quantum fluctuations to enhance the exploration of the solution space, addressing a persistent challenge in classical optimization algorithms. We present a framework that integrates quantum-like elements, specifically through the introduction of probabilistic adjustments to the gradient, to improve the robustness and efficiency of optimization in scenarios where traditional methods demonstrate limitations. The core claim is that this method can significantly improve performance, especially when dealing with highly non-linear systems, offering a promising pathway for tackling challenges across diverse fields. This work details the theoretical foundation, implementation, and preliminary results demonstrating the effectiveness of this quantum-inspired optimization strategy.

Sep 3, 20260 citations

Title: Quantum-Enhanced Topological Optimization Algorithms

Jincheng Zhang

This paper introduces a novel quantum-enhanced topological optimization algorithm designed to address complex optimization problems with inherent topological properties. Traditional optimization techniques often struggle with highly non-convex and sparse landscapes, limiting their effectiveness in diverse scenarios. We propose a quantum algorithm leveraging quantum entanglement to explore the solution space more efficiently and discover potentially globally optimal solutions. The core mechanism hinges on utilizing quantum entanglement to establish correlations between data points, enabling a probabilistic exploration of the solution space that surpasses the capabilities of classical methods. The proposed algorithm demonstrates promising results on benchmark topological optimization problems, showcasing the potential of quantum computation to revolutionize this field. This work contributes to the exploration of quantum algorithms for optimization, specifically focusing on topological optimization, and introduces a novel approach leveraging quantum entanglement for enhanced efficiency.

Sep 5, 20260 citations

Title: Adaptive Quantum State Optimization Algorithm

Jincheng Zhang

This paper introduces an adaptive optimization algorithm based on quantum state principles, leveraging quantum state superposition to model and solve complex systems effectively. The core mechanism centers around utilizing quantum state manipulation to accelerate optimization processes. This approach addresses existing algorithmic limitations by exploiting quantum properties, promising improved efficiency and scalability. The proposed algorithm seeks to overcome computational bottlenecks inherent in traditional optimization methods through the application of quantum entanglement. This research investigates the potential of quantum state representations for enhanced optimization capabilities, offering a novel methodology for tackling challenging optimization problems.

Sep 7, 20260 citations

Title: Quantum-Inspired Constraint Optimization with Adaptive Noise

Jincheng Zhang

Quantum-inspired constraint optimization presents a novel approach to solving complex optimization problems by leveraging quantum-mechanical principles. This paper introduces a framework for optimizing systems using quantum-inspired algorithms, incorporating adaptive noise to mitigate the risk of local optima. The core mechanism involves a 'quantum state exploration' – where the algorithm explores the solution space using quantum principles, guided by adaptive noise. This approach aims to overcome limitations of traditional optimization methods, particularly in scenarios where classical methods struggle to find optimal solutions. The study highlights the potential of quantum-inspired algorithms to enhance the robustness and efficiency of optimization processes, offering a promising pathway for tackling complex challenges across diverse domains.

Sep 5, 20260 citations

Title: Quantum-Enhanced Constraint Satisfaction in Complex Systems

Jincheng Zhang

Quantum-enhanced constraint satisfaction problems (CSPs) pose significant challenges for complex, multi-agent systems, particularly those exhibiting intricate dependencies and non-linear relationships. This research explores the application of a novel quantum optimization technique to address these hurdles, leveraging a hybrid quantum-classical approach to accelerate optimization and enhance solution space exploration. We propose a quantum entanglement mechanism to represent and exploit these dependencies, ultimately aiming to improve the performance of CSP solvers. This paper details the development of a new quantum algorithm, incorporating key elements of entanglement, hybrid computation, and optimization, to provide a significant advancement in the capabilities of solving complex CSPs. The investigation will focus on demonstrating the technique's efficacy through a simulated environment, highlighting its potential to overcome limitations of traditional approaches.

Aug 31, 20260 citations

Title: Quantum-Inspired Constraint Satisfaction for Complex Constraint Problems

Jincheng Zhang

Quantum-inspired constraint satisfaction problems (CSPs) represent a novel approach to tackling complex constraint sets, leveraging the principles of quantum mechanics to accelerate solution processes. This paper explores the potential of applying quantum-enhanced search algorithms and exploration to enhance the performance of CSPs, particularly in scenarios exhibiting significant computational complexity. We investigate the efficacy of incorporating quantum-inspired techniques to improve the efficiency and speed of solving problems with intricate constraint structures. This work centers around the concept of employing quantum phenomena – such as superposition and entanglement – within the search process to guide the exploration of the solution space, ultimately offering a promising path towards more robust and adaptable CSP solvers. The core mechanism focuses on adapting existing search strategies with quantum-inspired elements to overcome the limitations of classical approaches. We present preliminary results demonstrating the potential of this methodology, highlighting its ability to address challenges associated with large and intricate constraint sets.

Sep 4, 20260 citations