Modular quantum processors require a compiler to reason about two resources at the same time: local device connectivity and communication across QPUs. A mapping that is acceptable on a single coupling graph may be unsuitable for a modular machine if it creates excessive cross-QPU traffic, concentrates that traffic on a small number of interconnect links, or assigns many boundary qubits to a QPU with few communication ports. This paper presents QuPort, a Python and Qiskit-based compilation framework that studies this setting through an explicit three-level model: a weighted logical interaction graph, a directed physical coupling map, and an undirected QPU-level interconnect graph. The main partitioning method, TPCCAP, optimizes the implemented objective formed by weighted cut distance, communication-port overflow, and routed link-load congestion. The framework also includes heavy-edge clustering, balanced greedy partitioning, simulated-annealing refinement, communication-port-aware layout, extraction of remote two-qubit operations, local-only routing of per-QPU circuits, and topology-aware schedule estimation. The model is a compiler-level abstraction. It does not claim a calibrated hardware runtime or an implementation of a physical remote-gate protocol.

翻译：模块化量子处理器要求编译器同时权衡两类资源：本地设备连通性及跨QPU的通信。若单耦合图上可接受的映射方案产生过多跨QPU流量、将该流量集中于少数互连链路上，或为通信端口匮乏的QPU分配过多边界量子比特，则该方案可能不适用于模块化机器。本文提出QuPort——一个基于Python与Qiskit的编译框架，通过显式三层模型（加权逻辑交互图、有向物理耦合映射图、无向QPU级互连图）研究该场景。其核心划分方法TPCCAP优化了由加权切割距离、通信端口溢出与路由链路负载拥塞所构成的目标函数。该框架还包含重边聚类、平衡贪心划分、模拟退火精炼、通信端口感知布局、远程双量子比特操作提取、单QPU电路本地路由及拓扑感知调度估计。该模型属于编译器级抽象，不涉及标定硬件运行时间或物理远程门协议的实现。