Quantum-Enhanced Bayesian Inference for Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-enhanced Bayesian inference offers a promising approach to accelerate optimization of complex, high-dimensional problems, particularly those plagued by noisy data. This paper investigates the potential of leveraging quantum annealing or variational quantum eigensolver (VQE) to dramatically improve the speed and efficiency of optimization. We explore the core mechanism of utilizing these quantum algorithms to efficiently explore the solution space, addressing the limitations of classical optimization techniques. The paper begins with a review of Bayesian inference, its challenges, and the potential benefits of quantum computing. It then delves into the specific application of quantum annealing and VQE to optimization, highlighting key advantages in terms of exploration and convergence. The paper concludes with a discussion of the challenges and future directions of this research, emphasizing the need for further investigation and development.

DOI: 10.5281/zenodo.22194447

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