Quantum Decoherence Optimization Algorithm

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm, the Quantum Decoherence Optimization Algorithm (QDOA), designed to efficiently locate the optimal points of complex functions. The core principle of QDOA relies on harnessing the phenomenon of quantum decoherence. We formulate optimization problems as quantum circuits and leverage decoherence to progressively eliminate uncertain quantum states, thereby approximating the optimal solution. The algorithm's novelty lies in the direct application of quantum decoherence to optimization, potentially leading to significant improvements in both speed and accuracy compared to traditional optimization methods. The theoretical framework and underlying mechanisms are detailed, providing a foundation for further research and development in this area. The algorithm's performance is evaluated through analytical analysis and conceptual simulations. The key mathematical elements underpinning the QDOA are presented, including the decoherence rate, the state vector representation, and the iterative update rules. We demonstrate a potential pathway to achieving faster convergence in complex optimization scenarios.

DOI: 10.5281/zenodo.22158023

Related papers

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

Quantum Quantum Decoherence Calculation

Jincheng Zhang

Quantum Quantum Decoherence Calculation is a novel computational approach leveraging the unique properties of quantum coherence to precisely calculate complex quantum decoherence scenarios. Traditional quantum computing relies on classical algorithms, which are limited in their ability to accurately model and resolve the inherent decoherence processes that significantly impact quantum computation. This research proposes a new method employing quantum entanglement to construct a specialized calculation framework designed to capture and quantify decoherence effects, leading to substantially improved accuracy in simulating and predicting quantum systems. The core mechanism centers around strategically manipulating quantum states to generate and analyze decoherence patterns, ultimately providing a more reliable foundation for quantum algorithm design and optimization. The current method demonstrates superior performance in simulating complex decoherence dynamics compared to existing techniques, offering a significant advancement in the field of quantum computational fidelity. This work establishes a foundational basis for a new paradigm in quantum computation, emphasizing the crucial role of quantum coherence in achieving precise results.

Sep 6, 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

Exploring Quantum Machine Learning in Solving Complex Optimization Problems: Algorithms and Insights

Uzma Nawaz, Zubair Saeed, Kamran Atif

Optimization problems across domains such as logistics, finance, and artificial intelligence often involve complex and NP-hard formulations that are computationally challenging for classical algorithms due to scalability and efficiency limitations. The study aims to systematically investigate the role of Quantum Machine Learning (QML) in addressing complex optimization problems and to analyze its advantages over traditional optimization techniques. A comprehensive survey and comparative analysis of key QML algorithms, including Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Neural Networks (QNNs), and Quantum Support Vector Machines (QSVMs), is conducted by examining their working principles, optimization capabilities, and real-world applications. The findings indicate that QML algorithms demonstrate significant potential in exploring large solutions spaces efficiently, achieving faster convergence, and providing improved optimization performance compared to classical approaches, although challenges such as quantum noise, scalability, and hardware limitations remain. The novelty of this study lies in providing a unified and critical comparative framework that integrates multiple QML optimization algorithms, highlights their practical feasibility, and identifies key research gaps hindering their real-world deployment. The implications of this research provide valuable insights for researchers and practitioners in selecting appropriate QML techniques and emphasize the need for advancements in hybrid quantum -classical systems, algorithms design, and quantum hardware to enable practical large-scale optimization.

Scientific Journal of Computer ScienceFeb 19, 20260 citations

Quantum Adaptive Optimization Algorithm

Jincheng Zhang

This paper introduces a novel quantum adaptive optimization algorithm, leveraging quantum self-adaptivity to enhance algorithm efficiency and precision. Traditional optimization methods often rely on static parameter settings, which struggle to effectively handle complex system dynamics. This algorithm utilizes quantum entanglement and dynamic state changes to create a quantum self-adaptive optimization structure, providing a significant improvement over existing approaches. The core mechanism centers around constructing a quantum system that dynamically adjusts to the problem's characteristics, leading to faster convergence and improved solution quality. The paper details the algorithm's architecture, the underlying quantum principles, and preliminary experimental results demonstrating its effectiveness across a range of optimization problems.

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