Benchmarking Quantum Data Center Architectures: A Performance and Scalability Perspective

TL;DR

This paper systematically benchmarks four quantum data-center architectures, analyzing their performance and scalability under realistic hardware constraints, and provides insights for designing efficient distributed quantum computing systems.

Contribution

It offers a comprehensive performance comparison of four quantum data-center architectures considering quantum-specific effects, revealing how topology and physical parameters influence scalability.

Findings

Distributed quantum performance depends on topology, scheduling, and physical-layer parameters.

Optical-loss and reconfiguration delays significantly impact circuit execution latency.

Architectural properties like path diversity affect resource contention and scalability.

Abstract

Scalable distributed quantum computing (DQC) has motivated the design of multiple quantum data-center (QDC) architectures that overcome the limitations of single quantum processors through modular interconnection. While these architectures adopt fundamentally different design philosophies, their relative performance under realistic quantum hardware constraints remains poorly understood. In this paper, we present a systematic benchmarking study of four representative QDC architectures-QFly, BCube, Clos, and Fat-Tree-quantifying their impact on distributed quantum circuit execution latency, resource contention, and scalability. Focusing on quantum-specific effects absent from classical data-center evaluations, we analyze how optical-loss-induced Einstein-Podolsky-Rosen (EPR) pair generation delays, coherence-limited entanglement retry windows, and contention from teleportation-based non-local gates shape end-to-end execution performance. Across diverse circuit workloads, we evaluate how architectural properties such as path diversity and path length, and shared BSM (Bell State Measurement) resources interact with optical-switch insertion loss and reconfiguration delay. Our results show that distributed quantum performance is jointly shaped by topology, scheduling policies, and physical-layer parameters, and that these factors interact in nontrivial ways. Together, these insights provide quantitative guidance for the design of scalable and high-performance quantum data-center architectures for DQC.