Quantum computing has the potential to solve complicated problems that are impossible for classical servers. Nevertheless, the applications of current quantum processors are restricted by their limited qubit capacity. Distributed Quantum Computing (DQC) is promising to scale up the computing capability by interconnecting quantum processors and performing computing collectively. The network interconnecting quantum processors can impact the efficiency of DQC. In this paper, we analyze and compare the performance of various interconnection networks for DQC. First, we meticulously derive the success probabilities of entanglement generation and the fidelity of shared Bell states generated within three typical static networks: line, ring, and grid. In addition, we propose a switching network with a minimal number of switch stages and evaluate its performance in terms of probability and fidelity. Moreover, we conduct extensive simulations based on real-world parameters to compare the static and switching networks, and the results reveal that the switching network performs better and is more scalable.
Quantum computing has the potential to solve complicated problems that are impossible for classical servers. Nevertheless, the applications of current quantum processors are restricted by their limited qubit capacity. Distributed Quantum Computing (DQC) is promising to scale up the computing capability by interconnecting quantum processors and performing computing collectively. The network interconnecting quantum processors can impact the efficiency of DQC. In this paper, we analyze and compare the performance of various interconnection networks for DQC. First, we meticulously derive the success probabilities of entanglement generation and the fidelity of shared Bell states generated within three typical static networks: line, ring, and grid. In addition, we propose a switching network with a minimal number of switch stages and evaluate its performance in terms of probability and fidelity. Moreover, we conduct extensive simulations based on real-world parameters to compare the static and switching networks, and the results reveal that the switching network performs better and is more scalable.
Quantum computing, utilizing the unique properties of quantum mechanics, has the potential to revolutionize various fields. However, current quantum processors face challenges in scaling the number of qubits, limiting their practical applications. In response, Distributed Quantum Computing (DQC) has emerged as a promising paradigm where multiple interconnected Quantum Processing Units (QPUs) collaborate to execute quantum circuits. In this paper, we focus on designing networks to interconnect QPUs for the implementation of DQC. We find that in real-world experiments and systems, the photon collection and coupling efficiency is low, leading to significant performance degradation in direct connection networks. To address this limitation, we propose a novel multistage switching network tailored for DQC, which has low system complexity and high entanglement generation rates. The proposed switching network compriseslog2(N)stages andN/2binary switches at each stage, where N represents the number of QPUs. We prove that the proposed network is nonblocking and develop an efficient routing algorithm with a time complexity ofO(Nlog(N)). Additionally, we show the success probability of entanglement generation in the proposed switching network. Extensive simulations demonstrate that our network significantly outperforms the highly efficient circuit-switching Beneš network and three direct connection networks.
Quantum computers offer computational advantages for specific problems over classical systems [8], but today's devices are limited by qubit counts and susceptibility to noise. Distributed quantum computing (DQC) addresses these constraints by interconnecting quantum processors via quantum networks to enable cooperative execution of quantum algorithms [4]. DQC can be realized either through quantum teleportation [3], which transfers qubit states across nodes, or through remote gates [7], which enact quantum operations between spatially separated qubits while preserving their physical allocation. Both approaches depend on pre-shared entanglement, which must be distributed across potentially multi-hop quantum networks via entanglement swapping [11]. In realistic settings, DQC performance is influenced by factors such as entanglement fidelity, quantum memory coherence time, and routing path length, yet the impact of these parameters on computational fidelity remains insufficiently understood. This work investigates these effects through simulation of distributed Grover circuits executed over quantum networks with 1-4 routers, incorporating entanglement swapping, network delays, and varying physical constraints to assess how network-level properties affect circuit-level performance.
Distributed Quantum Computing (DQC) has the potential to solve industrial large-scale problems by connecting multiple small quantum processors together to form a larger computing system. Concerning the emerging distributed paradigm, a pivotal challenge lies in crafting specialized network topologies to establish efficient connections among quantum processors while minimizing communication costs. In this paper, we propose a novel DQC topology generation algorithm (DQC-TG) to create optimal and near-optimal network topologies for homogeneous and heterogeneous quantum computers, respectively. Furthermore, for specific quantum circuits requiring diverse communication demands between each pair of qubits, we extend the original algorithm into DQC- TG- Plus to design network topologies tailored for these circuits to further enhance the performance. We perform extensive simulations to evaluate the superiority of the generated network topology designs by our algorithms to baselines.
Quantum data-center (QDC) architectures aim to scale distributed quantum computing (DQC) by interconnecting multiple quantum processing units (QPUs), but their performance depends strongly on how algorithmic communication patterns interact with entanglement generation, switch reconfiguration, and network topology. This paper studies the Quantum Approximate Optimization Algorithm (QAOA) as a graph-structured optimization workload for QDC-based distributed quantum computing. We adapt QAOA to SwitchQNet, a distributed quantum compiler framework that schedules communication and entanglement generation over switch-based QDC networks, by adding a routing generator that converts graph-dependent two-qubit cost interactions into remote-CX communication requests across QPUs. Using this extension, we evaluate QAOA instances across Clos, fat-tree, and spine-leaf topologies, measuring communication latency, EPR-pair overhead, EPR wait time, retry overhead, and sensitivity to buffer size, look-ahead depth, communication-qubit count, EPR latency, and EPR fidelity assumptions. The results show that QAOA obtains modest but consistent latency reductions, highlighting its value as a diagnostic benchmark for studying the interaction between algorithm structure, entanglement management, and quantum-network architecture.
Quantum computers in the noisy intermediate-scale quantum (NISQ) era are constrained by limited qubit counts, imperfect gate fidelities, and short coherence times. NISQ-era computers often have too few qubits to run many algorithms, and those that do run often have limited quantum advantage. Distributed quantum computing (DQC) is a promising approach to overcome this, where multiple quantum processors are interconnected through shared entanglement. This work serves as a system overview of an entanglement distribution system. We achieve this by examining a zero-added loss multiplexing (ZALM) entanglement generation source, simulating multi-hop, repeater-less networks, and various routing algorithms to distribute entanglement, and real-hardware quantum experiments that reconstruct entanglement links of degraded fidelity and simulate classical-communication delay. Network simulations evaluate routing and spectrum-allocation algorithms under entanglement-generation-imposed rate constraints, by evaluating both fairness and throughput across network sizes and topologies. We identify two polynomial-time approximation algorithms that perform well, or better than others under these metrics. Using both simulations and quantum hardware, we evaluate how degraded entanglement and communication latency affect teleportation-based distributed multipartite-entanglement-state construction. The results reveal the coupled influence of source rate, routing efficiency, fidelity, and classical delay on networked entanglement distribution and DQC performance, providing a reproducible framework for both experimental and simulated studies of near-term DQC.
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.