Quantum-Enhanced Bayesian Network Inference (QBENI)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Abstract: Quantum-Enhanced Bayesian Network Inference (QBENI) presents a fundamentally new approach to Bayesian Network inference, integrating quantum-enhanced probabilistic modeling to achieve significantly improved accuracy and speed, particularly on large datasets. This paper introduces a novel algorithm that exploits quantum phenomena to create a more precise and efficient probabilistic model, addressing the limitations of traditional methods in complex scenarios. We detail the core mechanism, outlining the integration of quantum effects into the Bayesian Network framework and demonstrating the algorithm's efficacy through simulations and preliminary experiments.

DOI: 10.5281/zenodo.22191952

Related papers

Quantum-Enhanced Bayesian Network Inference for Complex Systems

Jincheng Zhang

Quantum-enhanced Bayesian Network Inference presents a novel approach to analyzing complex systems by leveraging the unique properties of quantum computation. This paper explores a quantum algorithm designed to significantly accelerate inference processes within Bayesian networks, addressing the limitations of classical methods when confronted with high-dimensional and complex dependencies. We detail the core mechanism, outlining how quantum algorithms will be employed to enhance the exploration of the network's state space and the efficient inference of its parameters. The proposed method offers a potential pathway for overcoming the computational bottlenecks inherent in traditional Bayesian network inference, particularly in scenarios with substantial data complexity. This research emphasizes the promise of quantum acceleration and improved accuracy in complex systems modeling.

Aug 31, 20260 citations

Title: Quantum-Enhanced Bayesian Inference for Bayesian Networks

Jincheng Zhang

Quantum-enhanced Bayesian inference offers the potential to significantly accelerate probabilistic predictions compared to classical Bayesian methods, particularly in complex Bayesian networks. This paper explores the implementation of a novel quantum-enhanced Bayesian inference algorithm, utilizing quantum computation to optimize Bayesian updates and inference. We present a framework for leveraging quantum hardware to enhance the accuracy and efficiency of probabilistic predictions within Bayesian network modeling. The core mechanism focuses on quantum-assisted Bayesian updates, designed to overcome computational bottlenecks inherent in traditional Bayesian inference. The proposed algorithm demonstrates improved accuracy and scalability through a rigorous analysis of its performance on a representative set of Bayesian network scenarios. The investigation delves into the potential advantages and challenges of integrating quantum computing into the Bayesian inference process.

Sep 6, 20260 citations

Quantum-Enhanced Bayesian Network Propagation for Complex Systems

Jincheng Zhang

Quantum-enhanced Bayesian Network Propagation for Complex Systems Abstract: Quantum-enhanced Bayesian Network Propagation for Complex Systems addresses the limitations of traditional Bayesian Network methods in handling complex, non-linear systems. This paper proposes a novel approach utilizing quantum-inspired algorithms to significantly improve the accuracy and efficiency of probabilistic updates within Bayesian Networks. The core claim is that this method, by combining quantum speed with Bayesian techniques, can overcome current bottlenecks in complex system modeling and analysis. This work investigates the potential of quantum phenomena to enhance Bayesian inference, offering a pathway toward more robust and accurate modeling of dynamic systems.

Sep 7, 20260 citations

Title: Quantum-Enhanced Bayesian Network Inference

Jincheng Zhang

Quantum-enhanced Bayesian Network Inference addresses the computational challenges associated with large, complex Bayesian networks through the integration of quantum algorithms. This paper introduces a novel technique leveraging Grover's search to accelerate conditional probability computations, ultimately enhancing inference efficiency. The core mechanism centers around quantum-enhanced probability updates, offering a potential paradigm shift in the processing of large network data. We explore the theoretical implications and preliminary experimental results demonstrating the efficacy of this approach, highlighting its potential to overcome limitations of traditional methods. The paper concludes with a discussion of future research directions, focusing on scalability and practical application.

Aug 29, 20260 citations

Quantum-Enhanced Bayesian Inference for Complex Networks

Jincheng Zhang

This paper investigates the potential of quantum computing to revolutionize Bayesian inference applied to complex networks. We propose a novel Bayesian inference algorithm, termed a 'Quantum Bayesian Network' (QBN), that integrates quantum mechanics principles to accelerate the identification of key nodes and connections within these networks. The core mechanism centers around leveraging quantum superposition and entanglement to enhance the probabilistic nature of Bayesian inference, leading to improved accuracy and efficiency. The proposed QBN offers a fundamentally different approach compared to traditional Bayesian methods, potentially unlocking new insights into network structure and dynamics. This work explores the theoretical foundations, potential implementation challenges, and preliminary results demonstrating the feasibility of this quantum-enhanced approach.

Aug 29, 20260 citations

Quantum-Enhanced Bayesian Network Propagation

Jincheng Zhang

This paper introduces a novel algorithm for Bayesian network propagation, utilizing quantum algorithms to accelerate inference and enhance the accuracy of network analysis. Traditional Bayesian network methods are computationally demanding, limiting their applicability in complex scenarios. We propose a framework that leverages quantum walk and quantum Monte Carlo to explore network structure and update probabilities with significantly improved efficiency. The core mechanism centers around employing quantum algorithms to tackle the inherent computational bottlenecks associated with Bayesian network inference. This approach promises a substantial improvement in speed and accuracy, opening doors to broader applications across various domains.

Sep 5, 20260 citations