基于量子模型的概率推理

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper investigates the potential of quantum computing to revolutionize probabilistic reasoning, focusing on the development of a novel system for complex model inference and prediction. Traditional probabilistic reasoning methods are limited by computational constraints, while quantum computing offers the promise of enhanced speed and accuracy. We propose a system that leverages quantum algorithms to simulate probabilistic behavior and guide prediction, with a specific emphasis on applications in finance and bioinformatics. This work explores the core mechanism and potential benefits of utilizing quantum computation for probabilistic inference. The paper outlines the key innovations and challenges associated with this approach, ultimately aiming to demonstrate the feasibility of quantum-enhanced probabilistic reasoning.

DOI: 10.5281/zenodo.22253329

Related papers

基于贝叶斯网络量子计算的概率推理算法

Jincheng Zhang

This paper introduces a novel probabilistic reasoning algorithm based on Bayesian networks for quantum computation. Quantum computation, while promising, often struggles with complex probabilistic inference, limiting the potential of quantum algorithms. This research explores a new approach that leverages the power of Bayesian networks to enhance quantum computation's reasoning capabilities. We propose a method that integrates quantum computation with Bayesian network inference, aiming to improve the accuracy and efficiency of probabilistic reasoning. The core mechanism centers around constructing and applying Bayesian networks to represent and reason about the state space of a quantum system, facilitating the exploration of multiple possible outcomes and guiding the algorithm towards optimal solutions. This work presents a framework for improving quantum algorithms through enhanced probabilistic inference, particularly relevant for complex problems requiring nuanced reasoning. The research will also examine the potential benefits and challenges of this approach.

Sep 3, 20260 citations

基于量子计算的概率推理算法

Jincheng Zhang

This paper presents a novel probabilistic reasoning algorithm based on quantum computing, aiming to address limitations of traditional algorithms in handling complex probabilistic scenarios. The core of this approach leverages quantum properties, specifically superposition and entanglement, to drastically accelerate the computation of probability distributions and inference. We demonstrate the algorithm's effectiveness through theoretical analysis and preliminary experimental validation, showcasing significant improvements in efficiency and accuracy compared to existing methods for specific probabilistic problems. The proposed algorithm's unique mechanism focuses on leveraging quantum entanglement for enhanced probability state manipulation, ultimately offering a paradigm shift in probabilistic computation.

Aug 29, 20260 citations

基于量子计算的自适应概率模型

Jincheng Zhang

This paper investigates the application of quantum computing to enhance probabilistic modeling, specifically focusing on the development of a self-adaptive probability model. The core challenge lies in efficiently simulating complex, dynamic probability processes, which is often computationally demanding. This research proposes a novel quantum algorithm that leverages quantum superposition and entanglement to represent and manipulate probability distributions, enabling dynamic adjustment and improved predictive accuracy. The proposed mechanism addresses limitations of traditional probabilistic models by harnessing quantum phenomena for enhanced computational efficiency and robust prediction. We present preliminary results demonstrating the model's capacity to handle complex scenarios with enhanced precision compared to existing approaches. This work lays the foundation for a new paradigm in probabilistic modeling, potentially revolutionizing simulations in fields requiring high accuracy and adaptability.

Sep 6, 20260 citations

基于有限域的量子纠缠的概率推理

Jincheng Zhang

This paper explores the potential of leveraging limited-domain quantum entanglement for probabilistic reasoning. Traditional approaches to quantum entanglement reasoning often rely on complex quantum computations, limiting the ability to effectively harness the inherent probabilistic nature of the phenomenon. We propose a novel framework utilizing limited-domain quantum entanglement, focusing on the probabilistic properties of entangled states. This framework offers improved efficiency and a more intuitive approach to probabilistic inference, potentially revolutionizing the way we reason about quantum systems. The core mechanism centers around encoding and manipulating entanglement states within a restricted Hilbert space, enabling probabilistic modeling and enhanced reasoning. We present a preliminary design and discuss the advantages and challenges of this approach, highlighting its potential for accelerating quantum computation and enhancing the precision of probabilistic analysis.

Aug 31, 20260 citations

量化时间因果关系建模算法

Jincheng Zhang

This paper introduces a novel quantum computing algorithm for the efficient modeling and inference of time-dependent causal relationships. Traditional probabilistic and statistical models struggle to effectively handle complex time-series data, particularly in simulating and predicting intricate systems. We propose a quantum-enhanced model that leverages quantum entanglement and quantum state manipulation to achieve a higher degree of precision in capturing causal dependencies. The core mechanism centers around constructing a quantum causal model, utilizing quantum algorithms for computational reasoning and ultimately, providing a more accurate representation of time- causality. This research explores the potential of quantum computing to revolutionize time-series analysis and forecasting, offering a significant advancement over existing approaches.

Aug 31, 20260 citations

基于量子态的概率推理框架

Jincheng Zhang

This paper introduces a novel probabilistic reasoning framework based on quantum states, designed to address complex inference problems. We propose a method that transforms the probabilistic inference process into a quantum computational paradigm, leveraging quantum state properties to enhance efficiency. The core mechanism involves quantum superposition and measurement, strategically applied to guide probabilistic reasoning. The research explores the potential of utilizing quantum states to overcome limitations of classical probabilistic inference techniques. This framework aims to provide a more robust and efficient approach for solving challenging problems across various domains, including data analysis, machine learning, and strategic decision-making. This work is a significant step towards harnessing the unique properties of quantum mechanics for enhanced computational capabilities.

Aug 29, 20260 citations