Quantum-Inspired Probabilistic Programming for Complex Systems

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper introduces a novel approach to probabilistic programming that draws inspiration from the principles of quantum mechanics. Traditional probabilistic programming methods struggle to efficiently represent and reason about complex systems characterized by inherent uncertainty, particularly when dealing with high-dimensional state spaces and intricate dependencies. Our proposed framework, termed "Quantum-Inspired Probabilistic Programming" (QIPP), addresses these limitations by incorporating concepts such as superposition and measurement. This allows for the simultaneous evaluation of multiple potential states, which are then probabilistically collapsed during inference, mirroring the process of quantum measurement. The core of QIPP lies in a redesigned syntax and runtime environment that natively supports these 'quantum-inspired' probabilistic representations. We demonstrate, theoretically, how this approach can enhance the simulation and reasoning capabilities for systems where classical probabilistic methods fall short, offering a fundamentally different paradigm for managing uncertainty. The presented methodology offers a path towards more robust and accurate modeling of complex systems across various domains.

DOI: 10.5281/zenodo.22139309

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