Probabilistic Programming for Quantum Algorithm Design

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper proposes a novel approach to quantum algorithm design by leveraging probabilistic programming. Traditional quantum algorithm design often relies on precise circuit specifications, which can be challenging due to the inherent uncertainty in quantum systems. We introduce a framework that allows users to define quantum algorithms as probability distributions, explicitly modeling the probabilistic nature of quantum computation. This framework enables the exploration of a wider range of potential solutions, facilitates optimization strategies based on probability, and provides a more intuitive way to design and understand quantum algorithms. The core concept is to represent quantum circuits not as deterministic operations, but as probability distributions over possible outcomes. This approach is particularly relevant for noisy intermediate-scale quantum (NISQ) devices where errors are unavoidable and inherent probability plays a crucial role. We outline the key components of this framework, including a probabilistic programming language extension and a mechanism for translating circuit descriptions into probability distributions. The work demonstrates the potential of probabilistic programming to overcome the limitations of traditional design methods and accelerate the development of practical quantum algorithms.

DOI: 10.5281/zenodo.22140240

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