基于动态量子态的概率模型

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel probabilistic model, termed the "Dynamic Quantum State-Based Probability Model," designed to predict and autonomously generate optimal strategies for complex system simulations. Traditional probabilistic models often struggle to effectively address complex environments due to their reliance on static parameters. This work introduces a method leveraging the dynamic evolution of quantum states to achieve enhanced predictive capabilities and automated strategic generation. We demonstrate the model's efficacy through illustrative examples, showcasing its ability to accurately forecast system behavior and generate optimal strategies across various simulated scenarios. The core of the model lies in its quantum state dynamics, which allows for a more nuanced and adaptable representation of system behavior compared to traditional approaches. This results in improved predictive accuracy and facilitates the automated generation of optimal strategies.

DOI: 10.5281/zenodo.22637073

Related papers

基于自适应量子算法的非线性概率模型

Jincheng Zhang

This paper presents a novel approach to probability modeling leveraging the inherent randomness of quantum computation. We introduce a dynamically adaptive probabilistic model, designed to enhance model precision and improve prediction accuracy through iterative optimization guided by quantum algorithms. The core mechanism centers around utilizing quantum randomness to dynamically adjust probability distributions, enabling the model to adapt to complex environments and achieve superior predictive capabilities. Traditional probabilistic models often struggle with this adaptability. This work explores how quantum algorithms can provide a more flexible and robust framework for probabilistic representation and refinement. The paper details the design of the algorithm, its mathematical foundations, and preliminary experimental results demonstrating its efficacy in specific scenarios.

Sep 7, 20260 citations

自适应量子概率场场论的非线性建模

Jincheng Zhang

This paper explores the development of a novel self-adaptive quantum probability field model. Traditional probabilistic models often rely on static parameter settings, limiting their ability to accurately represent complex systems. We propose a method leveraging quantum circuit simulation to dynamically adjust probability field parameters, enabling a more flexible and potentially more accurate system modeling process. The core mechanism involves iterative quantum circuit simulation based on input data and system state, allowing the model to continuously refine its representation of the underlying dynamics. This approach addresses a significant limitation of existing probabilistic frameworks, offering a pathway towards enhanced system understanding and predictive capability. We present a preliminary implementation demonstrating the efficacy of this self-adaptive approach in a simplified system, showcasing its potential for broader applicability.

Sep 1, 20260 citations

自适应量子态的量子模拟与优化

Jincheng Zhang

This paper introduces a novel approach to quantum simulation and optimization leveraging the dynamic nature of quantum states. Traditional quantum simulation methods often rely on static representations, limiting their ability to accurately model complex quantum systems. We propose a method that dynamically adjusts the quantum state throughout the simulation process, enabling a more refined and efficient representation of the system's behavior. This dynamic adaptation, coupled with a sophisticated optimization strategy, allows for the exploration of a broader range of possible solutions and improved model accuracy. The core mechanism centers around capturing the intricate dynamics of quantum systems through continuous state evolution, offering a significant advancement over static simulation techniques. This work demonstrates the potential of this approach to accelerate the design and analysis of complex quantum systems, particularly in areas of optimization and modeling.

Aug 31, 20260 citations

Quantum State-Based Dynamic Probability Model Optimization (QSPMO)

Jincheng Zhang

This paper introduces a novel dynamic probability model optimization algorithm, QSPMO, which leverages quantum state representations to dynamically adjust probability model parameters, leading to improved model prediction accuracy. Traditional probabilistic models often struggle with complex quantum systems due to their static nature. QSPMO addresses this limitation by employing quantum algorithms to iteratively refine probability parameter estimations based on the quantum state of the system. The core mechanism involves mapping the system's quantum state to a probabilistic representation, and then utilizing quantum optimization techniques to minimize the error between predicted and actual outcomes. This approach offers a promising pathway for enhancing predictive capabilities in various quantum applications.

Aug 31, 20260 citations

基于量子态的动态概率分布优化算法

Jincheng Zhang

This paper presents a novel dynamic probability distribution optimization algorithm based on quantum state evolution. Traditional probabilistic distribution optimization methods often rely on static parameter adjustments, limiting their effectiveness and generalization capabilities. We propose an algorithm that leverages the inherent quantum properties of probability distributions – specifically, their ability to adapt and evolve – to dynamically adjust the distribution parameters during the optimization process. The algorithm's core mechanism involves a quantum state representation, a feedback loop based on the state's evolution, and a controlled parameter update strategy. We demonstrate the algorithm's superiority through theoretical analysis and extensive numerical experiments, showcasing significant improvements in optimization performance and robustness compared to existing methods. The core claim is that the algorithm's quantum state adaptation allows for more efficient and reliable optimization compared to traditional approaches.

Sep 7, 20260 citations

Title: Dynamic Probability Distribution Modeling of Quantum States

Jincheng Zhang

This paper introduces a novel dynamic probability distribution modeling approach for quantum state analysis and long-term stability prediction. Traditional quantum state models often rely on static representations, limiting the ability to accurately simulate quantum system behavior across diverse environments. This work proposes a method that dynamically generates and analyzes quantum state probabilities, allowing for a more comprehensive and predictive understanding of quantum systems. The core mechanism involves constructing a dynamic probability distribution model, leveraging quantum state dynamics to emulate system behavior and forecast long-term stability. The method offers improved accuracy and robustness compared to conventional approaches, particularly in complex systems and varying environmental conditions. This research addresses a critical gap in quantum system modeling, providing a framework for more reliable and insightful predictions.

Sep 7, 20260 citations