🤖 AI Summary
This work addresses the challenge of error mitigation in distributed quantum computing, where communication-induced noise complicates the application of conventional zero-noise extrapolation (ZNE) techniques. The study presents the first systematic comparison between global and local ZNE strategies, evaluating their performance under heterogeneous local and network noise by partitioning quantum circuits into subcircuits and incorporating teleportation-based noisy communication primitives. Surprisingly, increasing the number of quantum processing units (QPUs) enhances the efficacy of global ZNE, achieving up to a 48% reduction in error in a six-QPU system. These findings uncover a novel trade-off among circuit structure, partitioning strategy, and network noise characteristics, while demonstrating strong scalability for global ZNE in distributed settings.
📝 Abstract
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.