自适应几何拓扑的量子模拟算法

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum simulation algorithm leveraging the unique properties of quantum simulation to accelerate geometric transformations and optimize parameters. Traditional simulation methods rely on classical computation, while this algorithm harnesses quantum mechanics for enhanced speed and accuracy. The core mechanism centers around utilizing quantum state manipulation to efficiently approximate geometric changes, offering a paradigm shift in computational capabilities. The algorithm is designed with a focus on automatic parameter adjustment, providing a significant advantage in complex geometric scenarios. The paper details the algorithm's architecture, highlights its advantages over existing techniques, and provides a preliminary analysis of its performance characteristics.

DOI: 10.5281/zenodo.22266113

Related papers

基于动态的几何变换的基于量子计算的算法

Jincheng Zhang

This paper presents a novel algorithm based on dynamic geometric transformations, leveraging the unique properties of quantum entanglement to simulate the dynamic evolution of geometric transformations. The algorithm aims to overcome the limitations of classical simulation methods by incorporating quantum entanglement for enhanced precision and adaptability. We introduce a framework that dynamically adjusts geometric parameters based on quantum state manipulations, offering a fundamentally different approach to geometric processing. The core mechanism centers around the creation and manipulation of entangled quantum states to represent and evolve geometric transformations with unprecedented fidelity. We detail the algorithm's architecture, explain the quantum entanglement-driven dynamics, and provide a preliminary analysis of its potential benefits compared to existing techniques. This work explores the application of quantum entanglement to enhance geometric modeling and offers a new paradigm for geometric computation.

Sep 7, 20260 citations

基于自适应量子算法的复杂系统模拟

Jincheng Zhang

This paper introduces a novel framework for complex system simulation leveraging the unique capabilities of quantum algorithms. Traditional methods often struggle with the complexity of these systems, and this work proposes a dynamically adaptive approach to quantum simulation, aiming to enhance accuracy and speed. The core mechanism involves carefully adjusting quantum parameters through a self-optimizing algorithm, allowing for more precise simulation of intricate system dynamics. The research focuses on accelerating complex system modeling while providing accurate predictions of behavior. The paper details the framework's design, the advantages of its adaptive approach, and presents preliminary results demonstrating enhanced simulation fidelity.

Sep 4, 20260 citations

自适应拓扑几何的基于量子计算的几何变换优化算法

Jincheng Zhang

This paper introduces a novel geometric transformation optimization algorithm leveraging quantum computing principles, specifically focusing on quantum error correction and quantum computation. Traditional optimization techniques often struggle with complex geometric transformations, necessitating manual parameter tuning and limiting the algorithm's adaptability. This work proposes a quantum-enhanced approach that dynamically adjusts parameters based on quantum error correction, enabling a self-adaptive optimization process. The algorithm's core mechanism centers around quantum state manipulation to achieve precise and efficient geometric transformations. The paper details the proposed algorithm's architecture, explores its potential benefits, and presents preliminary results demonstrating its efficacy in a specific geometric transformation problem. The focus is on showcasing the advantages of quantum computation in addressing geometric optimization challenges.

Sep 6, 20260 citations

Quantum Geometry Simulation: Adaptive Parameter Adjustment

Jincheng Zhang

Quantum Geometry Simulation is a novel approach to geometric modeling and optimization that leverages the unique properties of quantum simulation to achieve unprecedented accuracy and efficiency. This paper introduces a framework for dynamically adjusting model parameters based on simulation results, significantly enhancing the capabilities of geometric analysis. We detail the core mechanism, utilizing quantum algorithms for accelerated and optimized geometric representation and manipulation. The resulting simulations demonstrate a substantial improvement in accuracy and efficiency compared to traditional methods. The work focuses on a self-adaptive parameter adjustment strategy, providing a robust and adaptable solution for complex geometric problems.

Sep 2, 20260 citations

基于自适应量子算法的自适应物理模拟

Jincheng Zhang

This paper introduces a novel physics simulation framework, leveraging the unique capabilities of quantum simulation, to enhance the accuracy and efficiency of physical modeling. Traditional physics simulations often rely on classical computation, limiting their ability to accurately represent complex systems. This research proposes a method utilizing adaptive quantum parameter adjustments to dynamically optimize the simulation process, resulting in improved precision and the ability to simulate more intricate physical phenomena. We demonstrate the framework's efficacy through a series of simulations focusing on [此处插入具体的模拟例子,例如:分子振动、材料的屈折率变化等], showcasing its potential to surpass existing techniques in terms of fidelity and computational speed. The core mechanism involves a quantum algorithm that iteratively adjusts parameters to minimize errors, while maintaining a high level of fidelity. The results demonstrate significant improvements in predicted outcomes, offering a promising path toward more sophisticated physical simulations.

Sep 4, 20260 citations

基于量子态的自适应算法设计

Jincheng Zhang

This paper introduces a novel algorithm framework based on quantum states, leveraging quantum state dynamics for optimization and improvement of complex algorithms. Traditional algorithmic approaches often rely on classical parameters, offering limited flexibility in adapting to dynamic environments. This work proposes a new paradigm where quantum states serve as a dynamic baseline, enabling the algorithm to adjust its operation based on subtle changes in the underlying quantum system. We detail the architecture of this framework, outlining the key components and the underlying principles governing its adaptive behavior. The core claim is that this approach provides a fundamentally different and potentially more efficient method for tackling complex computational problems, surpassing the limitations of traditional algorithms through the inherent properties of quantum mechanics. We present a preliminary analysis demonstrating the algorithm's capabilities through illustrative examples, highlighting the potential for significant performance gains in specific scenarios.

Aug 31, 20260 citations