Adaptive Quantum System Modeling

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces an adaptive quantum system modeling framework leveraging quantum mechanics and machine learning to construct more accurate quantum system models. The core mechanism centers on dynamically adjusting the model based on the system's state, leading to improved quantum computing efficiency. This approach addresses limitations of traditional models, which rely on static parameters, and offers a novel methodology for enhanced quantum simulation capabilities. We present a preliminary implementation demonstrating the framework's effectiveness in modeling a specific quantum system, highlighting its potential for accelerating quantum algorithm development.

DOI: 10.5281/zenodo.22491034

Related papers

Quantum Machine Learning: Adaptive Learning Algorithms

Jincheng Zhang

This paper investigates the potential of quantum machine learning to revolutionize the field of machine learning through adaptive learning algorithms. Traditional machine learning algorithms often rely on static parameter settings, limiting their adaptability and potentially hindering optimal performance. We propose a novel quantum learning framework that leverages quantum entanglement and quantum computation to dynamically adjust model parameters during the learning process, resulting in enhanced learning efficiency and generalization capabilities. The core mechanism centers around utilizing quantum entanglement to create a dynamic feedback loop that continuously refines the model's parameters based on its internal state. We present a detailed theoretical analysis and preliminary experimental validation demonstrating the effectiveness of this approach in a simplified learning scenario. The study explores the potential advantages of quantum-enhanced learning over classical methods.

Sep 1, 20260 citations

Adaptive Quantum Machine Learning Model Optimization

Jincheng Zhang

This paper introduces an adaptive quantum machine learning model optimization method designed to dynamically adjust algorithm parameters based on model performance. Traditional machine learning optimization methods rely on manual parameter design, while this approach leverages the unique capabilities of quantum self-adapted machine learning models to achieve significantly improved model accuracy. The core mechanism involves a quantum optimization process that adapts to the model's internal state, allowing for efficient parameter refinement. This research explores the potential of quantum algorithms to enhance the optimization process, addressing key challenges in traditional machine learning. The paper presents a theoretical framework and preliminary experimental results demonstrating the effectiveness of this approach.

Sep 3, 20260 citations

Nonlinear Quantum System Simulation Adaptive Algorithm

Jincheng Zhang

This paper presents an adaptive algorithm framework based on nonlinear quantum system simulation, designed to optimize and improve the simulation of complex quantum systems. Traditional simulation methods rely on classical models, and this approach leverages the unique characteristics of nonlinear systems to offer a novel simulation methodology. The core mechanism focuses on constructing a framework that adapts to the dynamics of the system, allowing for targeted optimization and enhanced accuracy. The research explores the practical application of this framework through simulations of specific quantum systems, demonstrating its potential for addressing challenges in complex quantum modeling. The paper concludes with a discussion of the algorithm's design, implementation, and preliminary results, highlighting its strengths and potential impact on the field of quantum simulation.

Aug 31, 20260 citations

Quantum State-Adaptive Machine Learning Dynamic Adjustment

Jincheng Zhang

Quantum state-adaptive machine learning is a rapidly evolving field with the potential to revolutionize artificial intelligence. This paper explores the design and implementation of a novel algorithm, Quantum State-Adaptive Machine Learning Dynamic Adjustment, which leverages quantum state dynamics to dynamically adjust model parameters and enhance model performance. The core mechanism centers around exploiting quantum superposition and entanglement to iteratively refine model parameters based on the inherent quantum properties of the training data. This approach offers a fundamentally new paradigm for machine learning, potentially surpassing the limitations of traditional algorithms. We present a theoretical framework and preliminary experimental results demonstrating the algorithm's effectiveness in optimizing a specific machine learning task, illustrating its potential to significantly improve model accuracy and generalization.

Sep 4, 20260 citations

基于量子计算的自适应机器学习模型

Jincheng Zhang

This paper introduces a novel framework for machine learning, leveraging the unique properties of quantum computing to enhance both training and generalization. Traditional machine learning algorithms often rely on manual parameter tuning, which can be a time-consuming and inefficient process, especially when dealing with large datasets and complex models. We propose a "Quantum Machine Learning Self-Adaptive" mechanism that dynamically adjusts model parameters based on quantum computation's inherent adaptability, leading to significantly improved efficiency and robustness. The core mechanism centers around a quantum-enhanced optimization process that allows the model to explore a wider parameter space and avoid local optima. We demonstrate the effectiveness of this approach through simulations and preliminary experiments, showcasing its potential to overcome limitations of classical machine learning techniques. This research represents a significant step toward creating more efficient and adaptable machine learning algorithms, particularly in scenarios with substantial data and intricate model architectures.

Sep 1, 20260 citations

Quantum Machine Learning Model Building

Jincheng Zhang

This paper introduces a novel framework for adaptive model construction leveraging quantum machine learning algorithms. Traditional model building processes rely heavily on human design, limiting efficiency and potentially hindering the discovery of optimal models. Our proposed framework automates the model construction process, incorporating data feature analysis to iteratively refine the model's parameters and ultimately achieve superior performance. We present a foundational algorithm demonstrating the potential for significant efficiency gains and improved model quality in various machine learning tasks. The core mechanism centers around a quantum optimization loop that dynamically adjusts model parameters based on observed data characteristics, fostering a more adaptive and robust approach. We explore the theoretical implications and preliminary experimental results showcasing the framework's capabilities.

Aug 31, 20260 citations