Probabilistic Programming with Bayesian Quantum Circuits

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel approach to probabilistic programming by directly integrating Bayesian inference within the framework of quantum circuits. We propose a methodology where Bayesian priors and likelihood functions are encoded into the architecture and dynamics of quantum circuits, utilizing quantum interference to facilitate complex probabilistic computations. This hybrid quantum-classical framework represents a significant advancement over traditional classical Bayesian methods, particularly when dealing with high-dimensional data and limited sample sizes. The core claim rests on the efficient processing of probabilistic data and the learning of quantum models through this integrated approach. The theoretical underpinnings of this method are explored, outlining the key mechanisms and demonstrating the potential for surpassing existing probabilistic modeling capabilities. The paper focuses on the conceptual framework and highlights the potential for future research directions in this emerging field. ---

DOI: 10.5281/zenodo.22317034

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