To overcome the physical limitations of scaling monolithic quantum computers, distributed quantum computing (DQC) interconnects multiple smaller-scale quantum processing units (QPUs) to form a quantum network. However, this approach introduces a critical challenge, namely the high cost of quantum communication between remote QPUs incurred by quantum state teleportation and quantum gate teleportation. To minimize this communication overhead, DQC compilers must strategically partition quantum circuits by mapping logical qubits to distributed physical QPUs. Static graph partitioning methods are fundamentally ill-equipped for this task as they ignore execution dynamics and underlying network topology, while metaheuristics require substantial computational runtime. In this work, we propose a heuristic based on beam search to solve the circuit partitioning problem. Our time-aware algorithm incrementally constructs a low-cost sequence of qubit assignments across successive time steps to minimize overall communication overhead. The time and space complexities of the proposed algorithm scale quadratically with the number of qubits and linearly with circuit depth, offering a significant computational speedup over common metaheuristics. We demonstrate that our proposed algorithm consistently achieves significantly lower communication costs than static baselines across varying circuit sizes, depths, and network topologies, providing an efficient compilation tool for near-term distributed quantum hardware.

翻译：为克服单体量子计算机在扩展规模时面临的物理限制，分布式量子计算通过互连多个较小规模的量子处理单元构建量子网络。然而，该方法引入了一个关键挑战，即由量子态隐形传态和量子门隐形传态引发的远程QPU间量子通信的高成本。为最小化通信开销，DQC编译器必须通过将逻辑量子比特映射至分布式物理QPU来策略性地划分量子电路。静态图划分方法本质上不适用于此任务，因其忽略了执行动态与底层网络拓扑，而元启发式方法则需要大量计算时间。本文提出一种基于束搜索的启发式方法来解决电路划分问题。我们的时间感知算法通过增量式构建跨连续时间步的低成本量子比特分配序列，以最小化整体通信开销。所提算法的时间与空间复杂度随量子比特数量呈二次方增长，随电路深度呈线性增长，相比常见元启发式方法实现了显著的计算加速。实验表明，在不同电路规模、深度及网络拓扑下，我们提出的算法始终能获得远低于静态基线的通信成本，为近期分布式量子硬件提供了高效的编译工具。