Probabilistic Graph Models with Bayesian Inference and Quantum Computing Synergistic Optimization
Jincheng Zhang
This paper explores the synergistic combination of Bayesian inference and quantum computing for optimizing probabilistic graph models. Traditional Bayesian inference, particularly with complex graph structures, faces significant computational challenges. Quantum computing, leveraging its inherent parallelism, offers a promising avenue for accelerating these inference processes. We propose a framework that translates probabilistic graph models into quantum circuits, employing variational quantum algorithms such as Variational Quantum Eigensolver (VQE) or Quantum Approximate Optimization Algorithm (QAOA) to optimize the model parameters and expedite the inference. This approach aims to enhance both the accuracy and efficiency of Bayesian inference, addressing a critical bottleneck in various applications, including machine learning, statistical inference, and decision-making under uncertainty. The core claim of this research is the effective integration of Bayesian inference and quantum computing to accelerate the inference process. The underlying mechanism involves the transformation of probabilistic graph models into quantum circuits and utilizing quantum algorithms for optimization. This work represents a novel approach to tackling computationally intensive Bayesian inference problems, paving the way for more sophisticated and efficient probabilistic modeling. ---
•Sep 5, 2026•0 citations