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Adaptive Quantum Entanglement Nonlinear Equation Solver

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

This paper introduces an adaptive quantum entanglement nonlinear equation solver designed to overcome the limitations of traditional methods in solving complex nonlinear equations involving quantum entanglement. The core concept revolves around leveraging the unique properties of quantum entanglement to accelerate and enhance the solution process. We propose a novel algorithm that dynamically adjusts parameters to optimize the entanglement's influence during the equation solving process, resulting in significantly improved efficiency and accuracy compared to existing approaches. The study demonstrates the effectiveness of this approach through a series of simulations and analyses, validating its potential for tackling challenging problems in quantum physics and related fields.

DOI
10.5281/zenodo.22501894

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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.
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