Simulation results suggest that the lightweight strategy is sufficient for current DQC settings, offering a practical solution for managing remote-gate resource contention in distributed quantum circuits and improving overall system performance.
Abstract
In recent years, advances in quantum computing have been driven by substantial improvements in both the number and quality of qubits. As the field progresses, there is growing interest in interconnecting quantum systems to enable scalable computation through Distributed Quantum Computing (DQC) architectures. Consider a distributed quantum application executed across multiple Quantum Processing Units (QPUs) within a quantum data center, where remote gates require establishing entanglement between different QPUs. The creation of such end-to-end entanglement can lead to network congestion and resource contention. To address these challenges, we propose a resource management framework that maximizes fidelity-guaranteed throughput while satisfying dependency constraints. We first formulate the problem as a Mixed-Integer Linear Programming (MILP) model to provide a performance benchmark. Building on this, we develop efficient approximate scheduling algorithms that achieve performance comparable to the optimization solver. Although a trade-off exists between execution time and network throughput, simulation results demonstrate that one of the proposed strategies, Weighted Group Least Resource First (WGLRF), closely approximates the solver’s performance across most scenarios. These findings suggest that the lightweight strategy is sufficient for current DQC settings, offering a practical solution for managing remote-gate resource contention in distributed quantum circuits and improving overall system performance.
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.
Raymond P. H. Wu, Chathurika Ranaweera, Sutharshan Rajasegarar et al.· 0 citations
A comparative study of three entanglement management paradigms for multi-core quantum processors shows that adaptive entanglement managements can substantially improve communication efficiency in scalable quantum multi-core systems.
Rajeswari Suance, Anubhab Dutta, Ruchika Gupta et al.· 0 citations
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.
Samanvay Sharma, Siyuan Niu, Amin Taherkhani et al.· 0 citations
Quantum internet applications coordinate classical control, local quantum operations, and entanglement/communication across independent nodes under strict protocol ordering constraints. While Qoala and QNodeOS provide programming and execution abstractions for such applications, there is no compiler infrastructure that systematically lowers Qoala programs to executable node-level code while enabling optimization. We present the first multi-level compiler pipeline for Qoala quantum internet programs. The compiler lowers programs through three MLIR-based IRs that progressively expose hybrid control flow, explicit quantum memory, and Qoala block structure. On top of this pipeline, we implement (i) local peephole rewrites, and (ii) a block-reordering pass formulated as a MILP to reduce qubit lifetimes under precedence and resource constraints. We also provide static analyses for gate counts, qubit lifetimes, fidelity estimation, and quantum-memory efficiency. We evaluate on rotation-merging and blind quantum computing benchmarks using density-matrix simulation and IRderived estimates, showing that block reordering improves fidelity and memory reuse in latency-dominated regimes with modest compilation overhead.
Sacha Bernheim, Bart van der Vecht, Davide Ferrari et al.· 2026 IEEE International Conf...· 0 citations
Quantum computing is rapidly moving toward cloud-native, High-Performance Computing (HPC) models. However, current job submission systems rely on sequential, exclusive-use execution, causing severe resource under-utilization and excessive user wait times. This paper introduces QUDA (Quantum Unified Device Architecture), a distributed orchestration platform designed to solve this bottleneck by decoupling the control and execution planes. QUDA employs a master-worker hierarchy in the form of the Joint Unified Gateway (JUG) for global control and distributed Quantum Processor Schedulers (QuPS) for hardware-aware execution. By abstracting hardware complexity and leveraging OpenQASM 3.0, QUDA enables backend-agnostic portability across heterogeneous systems. We evaluate QUDA using W-state probes and combinatorial optimization benchmarks, demonstrating significant gains in system-wide throughput and qubit utilization over direct submission models. QUDA provides a scalable foundation for modern, distributed quantum workloads.
Alejandro Olvera, Harry Fu, Song Fu· 2026 International Conferenc...· 0 citations
Growing integration of distributed energy resources increases power-system variability and uncertainty. During disturbances, these effects can intensify generation-load imbalances and cascading failures. Controlled islanding limits their propagation by partitioning a compromised grid into connected, electrically sustainable islands. However, classical methods face rapidly growing computational costs as network size and island count increase. Quantum optimization offers an alternative for exploring this combinatorial partition space. Yet monolithic quantum formulations encode all assignment decisions in one circuit, causing qubit demand and circuit complexity to scale with network size. In this study, a qubit-bounded sequential distributed quantum approximate optimization algorithm (QAOA) framework is proposed to tackle coherent controlled islanding under limited quantum resources. It formulates the optimization as boundary-conditioned regional quadratic unconstrained binary optimization (QUBO) subproblems that are solved sequentially within a fixed qubit budget. Thus, circuit width remains independent of network size, with aggregate quantum workload scaling linearly on bounded-degree networks. Evaluation covers eleven IEEE systems from 9 to 300 buses using IBM quantum computing resources, with Gurobi and monolithic QAOA as references. Across all systems, the framework recovers feasible Gurobi-optimal partitions under noise, confirming the resilience of its solution quality. The results further show that the proposed method substantially reduces quantum-resource demand and circuit complexity relative to monolithic QAOA, allowing large islanding problems to be addressed within current hardware limits. The proposed framework provides a feasible and scalable pathway for quantum optimization in large-scale power systems.
Yuqi Jiang, Zhiding Liang, Qiang Guan et al.· 0 citations