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arXiv:2601.00214

DC-MBQC: A Distributed Compilation Framework for Measurement-Based Quantum Computing

Yecheng Xue, Rui Yang, Zhiding Liang, Tongyang Li

quant-phSubmitted Jan 1, 2026• Announced Jan 2, 2026• 0 citations
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Abstract

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 $7.46\times$ improvement on required photon lifetime and $6.82\times$ 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.

Comments
14 pages, 10 figures. To appear in the IEEE International Symposium on High-Performance Computer Architecture (HPCA) 2026

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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 7.46×7.46\times7.46× improvement on required photon lifetime and 6.82×6.82\times6.82× 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.
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 a7.46×7.46 \times7.46×improvement on required photon lifetime and6.82×6.82 \times6.82×speedup with 8 fully-connected QPUs, which further confirm the advantage of distributed quantum computing in photonic systems.
Quantum computing is a promising paradigm for problems that are challenging for classical machines, but realizing that promise requires far more qubits than a single processor can offer. Distributed quantum computing (DQC) scales out by connecting multiple quantum processing units (QPUs), at the cost of making entanglement the scarce resource: every remote gate consumes a Bell pair, and inter-QPU links generate Bell pairs at finite rates orders of magnitude slower than local gates. Since quantum programs are executed repeatedly, this rate bounds how fast shots complete and thus how fast results are obtained. Existing DQC compilers emit a single implementation per circuit, so throughput is capped by its busiest link while other links stay idle. We observe that alternative compilations of the same circuit are logically equivalent yet stress different links; executing them concurrently and pooling their samples converts idle Bell pairs into additional shots. We formulate the joint selection of compilations and allocation of shots under per-link Bell-pair capacities as Candidate-Constrained Max-Shot Allocation (CMA), prove it NP-hard, and solve it with a dynamic program and its approximate variant AppDP, a compact MILP, and a greedy heuristic Effi. In simulation on six-QPU networks, multi-compilation raises throughput by 2-4.5* over the single compilation and correspondingly improves output fidelity under equal Bell-pair budgets. On hardware, it increases measured fidelity by up to 93% and reaches the single-compilation fidelity in up to 10* less time. Scaling to 36 QPUs and 144-qubit circuits, MILP improves throughput over the single compilation by 76.8% on average across 48 configurations while Effi allocates in milliseconds.
Abstract Distributed quantum computing requires compiler infrastructure capable of partitioning quantum circuits across multiple quantum processing units (QPUs) and synthesizing entanglement-assisted communication channels. Existing quantum compilers—including Qiskit, t|ket⟩, and BQSKit—operate on single-QPU execution models and lack native support for multi-QPU intermediate representations. We present DQC, an MLIR-based distributed quantum compiler featuring a custom dqc dialect and a five-pass compilation pipeline: (1) interaction-graph-based qubit partitioning with weighted edge-cut minimization, (2) TeleGate synthesis replacing cross-QPU gates with EPR pair allocations and quantum teleportation sequences, (3) greedy operation reordering for communication pre-staging, (4) MPI dialect lowering for distributed runtime abstraction, and (5) LLVM IR emission targeting a quantum simulation runtime. We evaluate DQC on a distributed 4-qubit Quantum Fourier Transform and four additional benchmarks, demonstrating correct multi-QPU execution with verified quantum state output. To our knowledge, DQC is the first open-source MLIR-native compiler infrastructure targeting distributed quantum circuit execution with end-to-end compilation from high-level quantum IR to executable LLVM IR.
Measurement-Based Quantum Computing (MBQC) is inherently well-suited for Distributed Quantum Computing (DQC): once a resource state is prepared and distributed across a network of quantum nodes, computation proceeds through local measurements coordinated by classical communication. However, since non-local gates acting on different Quantum Processing Units (QPUs) are a bottleneck, it is crucial to optimize the qubit assignment to minimize inter-node entanglement of the shared resource. For graph state resources shared across two QPUs, this task reduces to finding bipartitions with minimal cut rank. We introduce a simulated annealing-based algorithm that efficiently updates the cut rank when two vertices swap sides across a bipartition, such that computing the new cut rank from scratch, which would be much more expensive, is not necessary. We show that the approach is highly effective for determining qubit assignments in distributed MBQC by testing it on grid graphs and the measurement-based Quantum Approximate Optimization Algorithm (QAOA).
Despite progress in quantum computing technology, the optimization of quantum circuit compilation for real-world quantum processing units (QPUs) [1] continues to pose a significant challenge. Current compilers use static gate-based models, which cannot adjust to changes in hardware limitations and noise fluctuations. In this study, we introduce Qukkos, a novel measurement-based quantum compilation system that optimizes resource utilization, improves circuit dependability, and achieves higher execution efficiency. To increase its adaptability for real-world quantum devices, Qukkos uses measurement-based quantum computing (MBQC) [2], [3], [4], [5], [6] to conduct dynamic circuit change. This system combines a quantum compiler with a quantum resource virtualization manager and standardized API interfaces to achieve seamless cross-platform execution alongside efficient task scheduling. Our experimental results across various QPU architectures show that Qukkos improves circuit depth reduction while increasing fidelity and speeding up compilation times relative to conventional methods. The modular design of Qukkos enables interoperability between superconducting qubits and trapped-ion systems as well as photonic quantum platforms and establishes it as a scalable solution for future quantum computing applications.
Distributed quantum computing (DQC) seeks to scale beyond the limits of monolithic processors by interconnecting multiple quantum processing units (QPUs) through entanglement-based links. Realizing this vision requires the co-design of hardware and software across the domains of quantum networking, compilation, and scheduling, yet existing tools remain fragmented between monolithic circuit compilers and long-distance quantum network simulators. We present an open-source, topology-informed framework for the compilation and scheduling of distributed quantum programs. Given an input circuit and a description of the target network, the framework partitions and reconstructs the circuit into a distributed program that respects the specified inter- and intra-QPU topology, covers cross-QPU operations through gate and state teleportation, and schedules the result under either deterministic or stochastic entanglement-generation models. By accepting and emitting standard OpenQASM, the framework interoperates with existing monolithic toolchains, and its standardized module interfaces allow partitioning and scheduling strategies to be interchanged and benchmarked. Using this framework, we show that the best-performing compilation strategy varies across the tested circuits and network configurations, and that both network topology and intra-QPU connectivity substantially affect the entanglement cost of execution. These findings underscore the need for a co-design approach to distributed quantum computing, which our framework is designed to support.