Probabilistic Programming for Simulating Quantum Circuit Evolution

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Simulating the evolution of quantum circuits is a computationally intensive task, fundamentally limited by the need to explore the exponentially large Hilbert space associated with quantum systems. This work introduces a novel approach utilizing probabilistic programming to efficiently model and simulate quantum circuit dynamics. The core idea involves representing quantum circuit operations as probabilistic functions, enabling the use of Monte Carlo methods for estimating circuit evolution with significantly reduced computational overhead. This approach provides a flexible and scalable framework for exploring complex quantum systems, particularly those where traditional deterministic simulation methods become intractable. The method's effectiveness is demonstrated through theoretical analysis and a conceptual outline of implementation, highlighting potential benefits and future research directions. Key mathematical concepts employed include probability theory, Markov chains, and Monte Carlo integration. The primary goal is to offer a practical alternative to standard quantum simulation techniques, particularly for scenarios requiring approximate solutions or exploration of large parameter spaces.

DOI: 10.5281/zenodo.22579195

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