Dynamic Programming for Quantum Circuit Optimization

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

Quantum circuit optimization is a critical challenge in the development and execution of quantum algorithms. The exponential growth in the complexity of quantum circuits necessitates efficient optimization techniques. This paper proposes a novel approach utilizing dynamic programming to systematically explore and minimize the resources (gate count and circuit depth) required for a given quantum circuit transformation. The core mechanism involves constructing a dynamic programming table to store the optimal solutions for subproblems, leveraging the overlapping subproblems inherent in the circuit transformation process. The algorithm systematically builds solutions from smaller circuit fragments, ultimately arriving at the globally optimal transformation. The potential scalability of this approach, combined with its ability to directly address the combinatorial nature of circuit optimization, offers a promising avenue for advancing quantum computing. This work provides a theoretical framework and outlines a detailed algorithm for dynamic programming-based quantum circuit optimization.

DOI: 10.5281/zenodo.22136490

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