基于动态概率的模态与量子纠缠的融合

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper explores a novel framework for quantum computation and probability computation leveraging dynamic probability. Traditional approaches often struggle to effectively utilize the inherent dynamism of probability in quantum systems. We propose a method that integrates dynamic probability with quantum entanglement, enabling enhanced efficiency and precision in both calculations and probabilistic outcomes. This integration addresses limitations of current quantum computing architectures by providing a more adaptable and robust system. The core mechanism hinges on dynamically adjusting quantum state parameters based on probabilistic feedback, leading to improved performance across a range of quantum algorithms. The resulting framework demonstrates significant potential for accelerating quantum algorithms and improving the reliability of probabilistic outcomes.

DOI: 10.5281/zenodo.22303385

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

基于自适应的概率场模拟的量子计算

Jincheng Zhang

This paper presents a novel quantum computing framework based on adaptive probability field simulation. Traditional quantum computation often relies on fixed parameters, limiting the potential for optimization. This work introduces a dynamically adjusted probability field, enabling significant efficiency gains in quantum computation. The framework leverages this adaptive property to accelerate quantum algorithms and explore novel computational pathways. The core mechanism centers around the construction of a "self-adaptive quantum computation" utilizing a dynamically evolving probability field. We analyze the impact of this approach on various quantum algorithms, demonstrating substantial improvements in performance and scalability. This paper proposes a robust and adaptable method for quantum computing, offering a pathway towards more efficient and versatile computation.

Sep 4, 20260 citations

Probability Matrix Dynamics and Quantum Computing Integration

Jincheng Zhang

This paper explores the integration of probabilistic matrix modeling with quantum computing to facilitate real-time system state modeling and prediction. Traditional probabilistic matrix approaches often struggle with dynamic environments, necessitating a novel dynamic adjustment mechanism. We introduce a framework that leverages quantum computing's inherent probabilistic properties to dynamically adjust matrix weights, enabling more accurate simulations and precise predictions. This work addresses the limitations of static parameter-based modeling by integrating quantum computation for enhanced system behavior representation. The core mechanism centers on adjusting probabilities based on quantum fluctuations, ultimately improving the responsiveness and accuracy of the modeling process. This represents a significant step towards adaptive system representation, particularly within complex, dynamic systems.

Aug 31, 20260 citations

基于量子纠缠的概率计算框架

Jincheng Zhang

This paper introduces a novel probabilistic calculation framework predicated on the principles of quantum entanglement. We propose a system for analyzing complex systems by leveraging the dynamic nature of entangled states to simulate probabilistic processes and predict outcomes. The framework emphasizes the unique capabilities of quantum entanglement in providing a fundamentally different approach to probability assessment compared to traditional, classical methods. This research explores the potential of quantum entanglement to enhance the accuracy and efficiency of probabilistic modeling, offering a fundamentally new perspective on the limitations of classical probability calculations. The core mechanism involves the creation and manipulation of entangled states, followed by a sophisticated algorithm to extract meaningful predictions from these states. This work lays the groundwork for a more robust and potentially transformative approach to probabilistic analysis across a range of complex systems.

Sep 1, 20260 citations

基于量子纠缠的自适应概率网络

Jincheng Zhang

This paper introduces a novel probabilistic network architecture, termed the "Quantum Entangled Adaptive Probability Network" (QEPN), that leverages the principles of quantum entanglement to enhance computational efficiency and precision. Traditional probabilistic networks struggle with complex calculations due to inherent limitations in parallel processing and the influence of sequential processing. The QEPN utilizes entanglement to create correlated quantum states, enabling instantaneous information transfer and accelerated computation. We propose a mechanism for dynamically adjusting the entanglement strength based on the input data, allowing the network to adapt to varying computational demands and optimize probabilities with unprecedented accuracy. This paper presents a detailed analysis of the QEPN's architecture, demonstrating its potential for significantly improving the speed and accuracy of various probabilistic tasks, including Monte Carlo simulations and Bayesian inference. The theoretical foundations of entanglement-enhanced computation are discussed, and preliminary experimental results showcase the network's capabilities. We conclude with a discussion of future research directions and potential applications.

Sep 6, 20260 citations

基于量子自适应的概率模型优化

Jincheng Zhang

This paper introduces a novel quantum self-adaptive probability model optimization algorithm. Traditional probabilistic models often rely on manual parameter adjustment, limiting the algorithm's effectiveness. This work leverages the unique properties of quantum entanglement and quantum computing to achieve a more robust and adaptable optimization process. We propose a quantum-enhanced optimization strategy that dynamically adjusts the model parameters based on quantum correlations, resulting in improved model reliability and generalization capabilities. The algorithm's core mechanism centers around establishing and manipulating quantum entanglement between model parameters, facilitating a more efficient and precise parameter search. This approach offers a promising alternative to classical optimization methods, particularly for complex probabilistic models requiring high accuracy and robustness. The results demonstrate the algorithm's effectiveness through a series of simulations and comparisons with established optimization techniques, showcasing its potential for significant advancements in probabilistic modeling.

Sep 5, 20260 citations