Dynamic Programming for Probabilistic Circuit Optimization

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum circuit optimization remains a critical challenge in the development of quantum computing. Traditional dynamic programming (DP) methods, while effective for deterministic circuit design, fail to adequately address the inherent probabilistic nature of quantum operations. This paper proposes a novel approach that extends DP algorithms by explicitly incorporating probabilistic models representing uncertainties in quantum gate operations and qubit measurements. The core idea is to leverage Monte Carlo simulations to estimate expected costs and rewards within the DP framework. This allows for a more realistic and potentially efficient solution to the circuit optimization problem, considering the statistical fluctuations that are fundamental to quantum computing. We demonstrate the feasibility of this method conceptually, outlining the key components and algorithmic steps. Further research and implementation will be necessary to fully realize the potential of this approach.

DOI: 10.5281/zenodo.22630359

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