Quantum-Inspired Probabilistic Programming for Uncertainty Quantification

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper introduces a novel approach to uncertainty quantification (UQ) by drawing inspiration from quantum mechanics. Traditional UQ methods, particularly Monte Carlo simulations, often struggle with the curse of dimensionality, exhibiting diminishing returns as the number of variables increases. We propose a framework utilizing simulated quantum measurement processes to efficiently explore probability spaces, aiming for significant improvements in both speed and accuracy. The core concept involves translating probabilistic programming logic into a sequence of simulated quantum measurements, mimicking the principles of superposition and entanglement. This approach moves beyond the limitations of classical sampling techniques and potentially unlocks exponential speedups in complex system modeling. The presented methodology focuses on a new programming language and runtime designed specifically for this quantum-inspired probabilistic programming paradigm. We explore the theoretical underpinnings and outline the key components of this system, highlighting the advantages it offers over conventional UQ methods. The design prioritizes computational efficiency and scalability, crucial aspects for tackling real-world problems characterized by high-dimensional uncertainty. The ultimate goal is to develop a robust and practical tool for accurately assessing risk and making informed decisions in complex, uncertain environments.

DOI: 10.5281/zenodo.22136380

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