EPR-pair consumption is commonly used as a communication-cost objective in distributed quantum computing, but minimizing EPR cost does not necessarily minimize distributed execution time. Despite its widespread use, the reliability of EPR count as a runtime surrogate has received limited direct characterization across different workloads and communication conditions. This work addresses this gap by systematically evaluating the relationship between EPR cost and runtime over all balanced two-QPU mappings of QFT, QAOA, and CDKM circuits. The results show that mappings with identical EPR cost can have substantially different execution times, lower-EPR mappings can be slower, and minimum-EPR mappings can be runtime-suboptimal. The reliability of EPR count is strongly workload dependent and also changes with the communication operating regime: increasing EPR-generation latency amplifies runtime differences hidden by equal EPR cost, while increased communication concurrency can reduce the ability of EPR count to preserve runtime ordering. These results show that EPR count and execution time are related but distinct optimization objectives, and identify regimes in which explicit communication-aware runtime modeling is necessary.
EPR-pair consumption is commonly used as a communication-cost objective in distributed quantum computing, but minimizing EPR cost does not necessarily minimize distributed execution time. Despite its widespread use, the reliability of EPR count as a runtime surrogate has received limited direct characterization across different workloads and communication conditions. This work addresses this gap by systematically evaluating the relationship between EPR cost and runtime over all balanced two-QPU mappings of QFT, QAOA, and CDKM circuits. The results show that mappings with identical EPR cost can have substantially different execution times, lower-EPR mappings can be slower, and minimum-EPR mappings can be runtime-suboptimal. The reliability of EPR count is strongly workload dependent and also changes with the communication operating regime: increasing EPR-generation latency amplifies runtime differences hidden by equal EPR cost, while increased communication concurrency can reduce the ability of EPR count to preserve runtime ordering. These results show that EPR count and execution time are related but distinct optimization objectives, and identify regimes in which explicit communication-aware runtime modeling is necessary.
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.
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.
Distributed quantum computing (DQC) is widely regarded as a promising approach to overcome quantum hardware limitations. A major challenge in DQC lies in reducing the communication cost introduced by remote CNOT gates, which are significantly slower and more resource-consuming than local operations. Existing DQC approaches treat the three essential components (qubit allocation, entanglement management, and network scheduling) as independent stages, optimizing each in isolation. However, we observe that these components are inherently interdependent, and therefore adopting a unified optimization strategy can be more efficient to achieve the global optimal solutions. Consequently, we propose UNIQ, a novel DQC optimization framework that integrates all three components into a non-linear integer programming (NIP) model. UNIQ aims to reduce the circuit runtime by maximizing parallel Einstein-Podolsky-Rosen (EPR) pair generation through the use of idle communication qubits, while simultaneously minimizing the communication cost of remote gates. To solve this NP-hard formulated problem, we adopt two key strategies: a greedy algorithm for efficiently mapping logical qubits to different QPUs, and a JIT (Just-In-Time) approach that builds EPR pairs in parallel within each time slot. Extensive simulation results demonstrate that our approach is widely applicable to diverse quantum circuits and QPU topologies, while substantially reducing communication cost and runtime over existing methods.
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.
In distributed quantum computing (DQC), executing monolithic quantum circuits across multiple interconnected quantum processing units (QPUs) requires dedicated communication qubits to generate and distribute entanglement. Because the number of physical qubits within a QPU is finite, a trade-off emerges where allocating more communication qubits increases the capacity of quantum channels for concurrent non-local operations, but reduces the number of computational qubits available for local gate operations. Distributed quantum compilation routinely ignores this channel capacity, while hardware architects lack a method to determine it prior to quantum circuit partitioning. Moreover, scheduling entanglement on demand introduces severe latency, whereas pre-fetching exposes stored pairs to decoherence. We propose an economic order quantity model from perishable inventory theory to optimize the trade-off between entanglement distribution latency and the time cost of decoherence. The resulting estimate is driven by algorithmic demand and physical constraints, offering a dual application for the hardware-software co-design of high-performance DQC: for hardware architects, it gives the optimal allocation of dedicated communication qubits in static heterogeneous architectures; for compiler developers, it gives the optimal number to reserve dynamically in homogeneous architectures.
As distributed quantum architectures begin to emerge, understanding the interaction between quantum circuit optimisation and circuit partitioning becomes increasingly important. In this work, we study how circuit optimisation influences distributed quantum workloads under system-level trade-offs. We compare three compilation strategies (global optimisation, local optimisation, and a hybrid approach) across a large benchmark suite of quantum algorithms. Using telegate-based partitioning, we evaluate the resulting distributed circuits in terms of gate counts, circuit depth, the number of induced non-local gates, and compilation overhead, thereby approximating computational, communication, and classical preprocessing costs. Our results show that circuit optimisation does not uniformly benefit distributed execution. Global optimisation minimises computational resources and achieves the lowest compilation overhead. Local optimisation can reduce communication cost even though it is not explicitly communication-aware. The hybrid strategy can simultaneously reduce both computational and communication overhead, but at the expense of significantly increased compilation time.