Errors are the primary bottleneck preventing practical quantum computing. This challenge is exacerbated in the distributed quantum computing regime, where quantum networks introduce additional communication-induced noise. While error mitigation techniques such as Zero Noise Extrapolation (ZNE) have proven effective for standalone quantum processors, their behavior in distributed architectures is not yet well understood. We investigate ZNE in this setting by comparing Global optimization (ZNE is applied prior to circuit partitioning), against Local optimization (ZNE is applied independently to each sub-circuit). Partitioning is performed on a monolithic circuit, which is then transformed into a distributed implementation by inserting noisy teleportation-based communication primitives between sub-circuits. We evaluate both approaches across varying numbers of quantum processing units (QPUs) and under heterogeneous local and network noise conditions. Our results demonstrate that Global ZNE exhibits superior scalability, achieving error reductions of up to $48\%$ across six QPUs. Moreover, we observe counterintuitive noise behavior, where increasing the number of QPUs improves mitigation effectiveness despite higher communication overhead. These findings highlight fundamental trade-offs in distributed quantum error mitigation and raise new questions regarding the interplay between circuit structure, partitioning strategies, and network noise.

翻译：误差是阻碍实用量子计算发展的主要瓶颈。在分布式量子计算体系中，这一挑战因量子网络引入额外通信噪声而进一步加剧。虽然零噪声外推等误差缓解技术在独立量子处理器上已被证明有效，但其在分布式架构中的行为机制尚未得到充分理解。本研究通过对比全局优化与局部优化两种方案，探究ZNE在分布式环境中的应用特性：全局优化在电路分割前实施ZNE，而局部优化则对各子电路独立应用ZNE。我们首先对整体电路进行分割，随后通过在各子电路间插入基于量子隐形传态的噪声通信原语，将其转化为分布式实施方案。通过在不同数量的量子处理单元及异构的本地与网络噪声条件下进行评估，实验结果表明全局ZNE展现出更优的可扩展性，在六QPUs系统中实现高达$48\%$的误差降低。值得注意的是，我们观察到反直觉的噪声行为：尽管通信开销增加，但增加QPUs数量反而提升了缓解效果。这些发现揭示了分布式量子误差缓解中的本质权衡关系，并对电路结构、分割策略与网络噪声间的相互作用机制提出了新的科学问题。