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.
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.
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.
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.
Distributed Quantum Computing (DQC) addresses the physical scaling limitations of monolithic quantum processors by networking modular Quantum Processing Units (QPUs). Efficient execution of quantum algorithms on DQC architectures requires compiling them across QPUs while minimizing inter-QPU communication bottlenecks, primarily through circuit partitioning. However, current evaluations of state-of-the-art partitioning heuristics focus primarily on the total entanglement cost of the partitions, failing to capture the broader structural and temporal overheads introduced by distributed network constraints. This paper addresses this evaluation gap by applying established monolithic benchmarking metrics to partitioned distributed circuits to quantify the performance impact of network constraints. Using an open-source, automated evaluation pipeline, we systematically assess diverse partitioning algorithms across standardized workloads and quantum network topologies. Our empirical results reveal that partitioning algorithms with comparable entanglement costs can still introduce drastically different physical execution penalties. By exposing these hidden trade-offs, such as severe increases in circuit depth and substantial reductions in gate density, this study demonstrates that comprehensive circuit-level metrics are essential for guiding the future design of DQC compilers.
In many practical applications, quantum algorithms require several qubits, significantly more than those available with current noisy intermediate-scale quantum processors. Distributed quantum computing (DQC) is considered a scalable approach to increasing the number of available qubits for computational tasks. In the DQC setting, a quantum compiler must find the best partitioning for the quantum algorithm and then perform smart non-local operations scheduling to optimize the consumption of Einstein-Podolsky-Rosen (EPR) pairs. In this work, the focus is on minimizing the use of EPR pairs when the circuit structure allows for multiple non-local gates to utilize a single TeleGate operation. This is achieved by using a greedy algorithm that explores the circuit and groups together the gates that could share an EPR pair while also changing the order of commutative gates when necessary. With this preliminary pass, the compiled circuits show reduced depth and EPR usage. Since the quality of each EPR pair quickly deteriorates, the number of non-local gates using the same EPR pair should also be bounded. This means that, depending on the features of the target quantum network, the user can achieve different levels of optimization. Here, it is shown that this approach brings benefits even while assuming a low EPR pair lifetime.
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.
Distributed quantum computing (DQC) is a promising near-term approach for executing circuits that exceed the capacity of a single quantum processing unit (QPU). Prior work has made significant progress on circuit partitioning and reconstruction, but practical DQC requires more than cutting a circuit into smaller pieces. A runtime system must determine whether to execute the circuit intact, cut it for sequential execution on one QPU, or distribute it across multiple QPUs, while accounting for queueing, communication, reconstruction, and hardware constraints. We present a resource-aware scheduler for DQC that compares these execution alternatives at runtime using explicit models of QPU execution, communication, and host-side reconstruction, together with hardware-informed timing derived from simulated IBM QPUs. By treating communication and reconstruction as first-class scheduling costs, the scheduler selects execution plans using a more realistic view of end-to-end overhead than cutting-only approaches. Across our experiments, reconstruction accounts for over 83% of cutting overhead, distributed execution reduces P90 latency by up to 39% relative to uncut single-QPU execution, and scheduling overhead itself remains negligible, never exceeding 0.038% of end-to-end job latency. Together, these results show that DQC must be treated as a runtime scheduling problem over both quantum and classical resources, not just circuit partitioning.