Joint Optimization of Circuit Transformation and Qubit Mapping for Distributed Quantum Computing

Abstract

Distributed Quantum Computing (DQC) scales quantum computing capabilities by interconnecting multiple Quantum Processing Units (QPUs) for collaborative computation, but faces the critical challenge of high entanglement costs associated with remote gate operations. Circuit transformation and qubit mapping are critical components of quantum circuit compilation for minimizing entanglement cost, which are interdependent in DQC. Existing approaches that treat circuit transformation and qubit mapping as separate optimization problems fail to account for their fundamental interdependence, resulting in suboptimal entanglement utilization and degraded performance. This paper presents the first optimization framework for circuit transformation and qubit mapping tailored for DQC. To address this joint optimization problem, we propose Qmactr, a two-phase reinforcement learning approach. The first phase trains a qubit mapping agent, and the second phase trains a circuit transformation agent that utilizes the mapping agent as a reward oracle. Extensive evaluation results demonstrate that the proposed approach reduces entanglement consumption by up to 35% compared to state-of-the-art sequential methods while achieving superior circuit fidelity.