基于自适应的量子态的自适应建模

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum state modeling based on adaptive parameter adjustment. Traditional quantum state modeling methods are often static, limiting their ability to accurately represent complex quantum systems. This work proposes a dynamically adjusted model that leverages self-adaptive parameter control, resulting in improved model precision and efficiency. The core mechanism involves adjusting quantum state parameters based on input data, enabling a more flexible and adaptable modeling process. We demonstrate the effectiveness of this method through a series of simulations, showcasing its superior performance compared to static modeling techniques in a range of scenarios. This innovation holds significant potential for advancing the field of quantum information processing.

DOI: 10.5281/zenodo.22263688

Related papers

基于自适应的量子态的自适应映射

Jincheng Zhang

This paper introduces a novel approach to quantum state mapping leveraging adaptive parameter adjustment. Traditional quantum state mapping methods are often static, offering limited flexibility and hindering optimization. We propose a system that dynamically adjusts quantum state parameters based on input data, enabling a more efficient mapping process. The core mechanism involves a feedback loop that continuously refines the quantum state to achieve the desired outcome. This adaptive approach offers a significant improvement over existing techniques, particularly in scenarios requiring complex mappings and dynamic parameter adjustments. The proposed method demonstrates improved performance through rigorous analysis and simulations, highlighting its potential for advancing quantum computing.

Sep 3, 20260 citations

基于自适应的量子态模拟算法

Jincheng Zhang

This paper introduces a novel quantum state simulation algorithm based on adaptive parameter adjustment. Traditional quantum state simulation methods are often static, limiting efficiency. This work proposes a dynamic adjustment strategy that modifies simulation parameters in response to changes in the quantum state, leading to improved accuracy and computational efficiency. We demonstrate the effectiveness of this approach through a series of simulations, showcasing its ability to surpass the performance of static methods in certain scenarios. The core mechanism involves continuously monitoring the quantum state and adjusting parameters to maintain optimal simulation fidelity. The resulting algorithm offers a significant advancement in the field of quantum state simulation.

Aug 31, 20260 citations

基于自适应量子模型的扩展 - 复杂系统的建模

Jincheng Zhang

This paper introduces a novel approach to quantum system modeling, termed "Adaptive Quantum Model Extension" (AQME), that leverages self-adaptive parameter adjustments to enhance the representation and accuracy of complex systems. Traditional quantum modeling often relies on static parameter settings, limiting its ability to accurately capture the intricacies of intricate systems. AQME overcomes this limitation through a dynamic adjustment mechanism, enabling a more refined simulation process. We demonstrate the efficacy of this approach on a series of benchmark systems, showcasing significant improvements in model fidelity and computational efficiency. The core of the algorithm lies in iteratively refining model parameters based on the system's dynamics, leading to a more robust and precise representation. The benefits of this extended method are evident in improved simulation results across diverse systems, highlighting its potential for advancing the field of complex system modeling.

Sep 4, 20260 citations

自适应几何的量子态的计算

Jincheng Zhang

This paper explores the potential of utilizing dynamic adaptive mechanisms to enhance the calculation of quantum states. Traditional quantum state estimation methods often rely on static models, limiting their ability to accurately represent complex quantum systems. We propose a novel approach that dynamically adjusts the parameters of a quantum state representation, leveraging the inherent self-adaptation properties of quantum systems. This method aims to improve prediction accuracy by capturing the subtle nuances of quantum behavior, offering a significant advancement in computational capabilities. The core claim is that this dynamic adaptive system will yield superior results compared to static approaches, particularly for intricate quantum systems.

Sep 1, 20260 citations

基于自适应的量子态的模拟与控制

Jincheng Zhang

This paper presents a novel approach to quantum state simulation and control leveraging adaptive parameter adjustment. Traditional quantum simulation methods often rely on fixed parameters, limiting accuracy. We introduce a dynamically adjusted parameter strategy to refine the simulation and control processes, ultimately increasing the precision of quantum computations. The core mechanism involves an adaptive algorithm that continuously monitors and adjusts parameters to optimize the simulation's fidelity. This approach offers a significant improvement over static parameter settings, leading to enhanced quantum information processing. The theoretical and experimental results demonstrate the effectiveness of this method in simulating complex quantum systems, showcasing a notable increase in precision.

Aug 31, 20260 citations

Dynamic Adaptive Quantum State Optimization

Jincheng Zhang

This paper introduces a novel dynamic self-adaptive quantum state optimization technique. Traditional quantum state optimization methods rely on static parameter settings, which are often inadequate for dynamic environments. Our approach leverages a dynamic adjustment of quantum state parameters, enabling precise control and optimization of quantum states. We propose a method that continuously refines the quantum state based on input data, achieving a significant improvement in state fidelity and control compared to static optimization techniques. The core mechanism revolves around a feedback loop that adjusts parameters to minimize errors and maximize desired outcomes. The resulting system demonstrates superior performance in various quantum control applications.

Sep 3, 20260 citations