Distributed Quantum Computing (DQC) provides a scalable architecture by interconnecting multiple quantum processor units (QPUs).Among various DQC implementations, quantum data centers (QDCs) -where QPUs in different racks are connected through reconfigurable optical switch networks -are becoming feasible in the near term.However, the latency of cross-rack communications and dynamic switch reconfigurations poses unique challenges to communications in QDCs, significantly increasing the overall latency, thereby also reducing the overall fidelity.In this paper, we address these challenges by introducing a novel compiler that optimizes scheduling of communications across the program and network layers.Our evaluation shows that it reduces the overall latency by 8.02× over prior approaches with a small overhead and can be integrated with quantum error correction (QEC) to facilitate fault-tolerant quantum computing (FTQC).We have open-sourced our codes at https://zenodo.org/records/15377656.
Distributed Quantum Computing (DQC) provides a scalable architecture by interconnecting multiple quantum processor units (QPUs).Among various DQC implementations, quantum data centers (QDCs) -where QPUs in different racks are connected through reconfigurable optical switch networks -are becoming feasible in the near term.However, the latency of cross-rack communications and dynamic switch reconfigurations poses unique challenges to communications in QDCs, significantly increasing the overall latency, thereby also reducing the overall fidelity.In this paper, we address these challenges by introducing a novel compiler that optimizes scheduling of communications across the program and network layers.Our evaluation shows that it reduces the overall latency by 8.02× over prior approaches with a small overhead and can be integrated with quantum error correction (QEC) to facilitate fault-tolerant quantum computing (FTQC).We have open-sourced our codes at https://zenodo.org/records/15377656.
Distributed Quantum Computing (DQC) enables scalability by interconnecting multiple QPUs. Among various DQC implementations, quantum data centers (QDCs), which utilize reconfigurable optical switch networks to link QPUs across different racks, are becoming feasible in the near term. However, the latency of cross-rack communications and dynamic reconfigurations poses unique challenges to quantum communication, significantly increasing the overall latency and exacerbating qubit decoherence. In this paper, we introduce a new optimization space to parallelize cross-rack communications and avoid frequent reconfigurations, which incurs additional in-rack communications that can be further minimized. Based on this, we propose a flexible scheduler that improves communication efficiency while preventing deadlocks and congestion caused by the flexibility. Through a comprehensive evaluation, we show that our approach reduces the overall latency by a factor of 8.02, thereby mitigating qubit decoherence, with a small overhead.
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.
The development of quantum computers, which are effective for problems where computational complexity increases exponentially with input size, such as combinatorial and optimization problems, is progressing rapidly. Quantum computers perform calculations using qubits, which have properties such as superposition and entanglement. However, qubits are known to be extremely susceptible to noise, and the realization of fault-tolerant quantum computing (FTQC), which performs calculations on error-correction codes, is highly anticipated. Our research group is working to achieve quantum error correction for FTQC using an FPGA cluster connected to qubit-control units in a single interconnection network. A large number of physical qubits are necessary to realize a practical error-correction code, and these require a large number of control units. The original plan was to use high-speed Ethernet for the interconnect, but problems arose due to bandwidth saturation caused by increased control units and a large communication delay relative to the qubit coherency time. This paper describes the development of a frame aggregator to address these problems, a dedicated switch for the FPGA cluster, and the results. Both mechanisms offer high scalability with latency equivalent to or lower than that of commercially available Ethernet switches and will make a significant contribution towards the realization of FTQC.
Distributed quantum computing (DQC) is a promising technique for scaling up quantum systems. While significant progress has been made in DQC for quantum circuit models, there exists much less research on DQC for measurement-based quantum computing (MBQC), which is a universal quantum computing model that is essentially different from the circuit model and particularly well-suited to photonic quantum platforms. In this paper, we propose DC-MBQC, the first distributed quantum compilation framework tailored for MBQC. We identify and address two key challenges in enabling DQC for MBQC. First, for task allocation among quantum processing units (QPUs), we develop an adaptive graph partitioning algorithm that preserves the structure of the graph state while balancing the workload across QPUs. Second, for inter-QPU communication, we introduce the layer scheduling problem and propose an algorithm to solve it. Regrading realistic hardware requirements, we optimize the execution time of running quantum programs and the corresponding required photon lifetime to avoid fatal failures caused by photon loss. Our experiments demonstrate a 7.46× improvement on required photon lifetime and 6.82× speedup with 8 fully-connected QPUs, which further confirm the advantage of distributed quantum computing in photonic systems. The source code is publicly available at https://github.com/qfcwj/DC-MBQC.
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.
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.