Probabilistic Programming for Quantum Circuit Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum circuit optimization is a critical step in realizing the full potential of quantum computing. However, traditional optimization methods often struggle to account for the inherent uncertainties present in quantum systems, such as noise, decoherence, and incomplete knowledge of circuit parameters. This paper proposes a novel approach leveraging probabilistic programming to address this challenge. We demonstrate how probabilistic programming languages can be used to model and reason about these uncertainties, leading to more robust and efficient optimization strategies. The core concept involves defining a probabilistic model of the quantum circuit optimization problem, incorporating parameters with associated probability distributions. This allows for the exploration of the solution space in a statistically informed manner, ultimately leading to better-optimized quantum circuits. The developed framework offers a pathway for more reliable and adaptive optimization, particularly relevant for near-term quantum devices where noise and errors are unavoidable.

DOI: 10.5281/zenodo.22184217

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