Dynamic Programming for Quantum Circuit Optimization with Error Mitigation

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum circuit optimization is a critical step in realizing the full potential of quantum computing. Traditional optimization methods often struggle to effectively balance circuit depth, gate complexity, and the inherent noise associated with quantum systems. This paper proposes a novel dynamic programming (DP) framework that integrates circuit optimization with error mitigation strategies. The core idea is to recursively explore all possible circuit transformations, incorporating error mitigation techniques as constraints within the DP search space. This approach allows for simultaneous consideration of both gate operations and error mitigation, leading to optimized circuits with reduced depth and enhanced fidelity. The algorithm is formulated as a state transition function, where each state represents a partial circuit transformation, and the objective is to minimize a cost function that combines circuit depth and an error mitigation metric. We demonstrate the feasibility and potential efficiency of this framework through a conceptual analysis and outline the key components of the implementation. Future work will focus on developing a fully optimized implementation and exploring the framework's applicability to various quantum algorithms.

DOI: 10.5281/zenodo.22267463

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