Scaling quantum computing beyond a single device requires networking many quantum processing units (QPUs) into a coherent distributed system. However, most quantum network proposals focus on physical link technologies or point-to-point entanglement generation, leaving the adaptive orchestration of entanglement as a shared resource largely unexplored. We introduce the Modular Entanglement Hub (ModEn-Hub), a hub-and-spoke photonic interconnect paired with a real-time orchestrator that centralizes entanglement generation, routing, and caching across heterogeneous QPUs. ModEn-Hub manages the full Bell-pair lifecycle—deciding which links to entangle, when to schedule teleportation-based non-local gates, and how to allocate a limited cache of stored ebits under loss and decoherence constraints. To illustrate the potential benefits, we perform a lightweight, reproducible Monte Carlo study based on simple but realistic models of photon loss, finite memory lifetime, and bounded time budgets, comparing a naïve sequential strategy to an orchestrated policy with limited parallel attempts and opportunistic reuse of cached entanglement. In our experiments across small-to-moderate network sizes, ModEn-Hub-style orchestration maintains substantially higher teleportation success probabilities than the baseline, albeit at the cost of more entanglement generation attempts. These results provide high-level evidence that adaptive, centrally coordinated resource management is a promising architectural pattern for scalable, near-term distributed quantum computing.
Scaling quantum computing beyond a single device requires networking many quantum processing units (QPUs) into a coherent distributed system. However, most quantum network proposals focus on physical link technologies or point-to-point entanglement generation, leaving the adaptive orchestration of entanglement as a shared resource largely unexplored. We introduce the Modular Entanglement Hub (ModEn-Hub), a hub-and-spoke photonic interconnect paired with a real-time orchestrator that centralizes entanglement generation, routing, and caching across heterogeneous QPUs. ModEn-Hub manages the full Bell-pair lifecycle—deciding which links to entangle, when to schedule teleportation-based non-local gates, and how to allocate a limited cache of stored ebits under loss and decoherence constraints. To illustrate the potential benefits, we perform a lightweight, reproducible Monte Carlo study based on simple but realistic models of photon loss, finite memory lifetime, and bounded time budgets, comparing a naïve sequential strategy to an orchestrated policy with limited parallel attempts and opportunistic reuse of cached entanglement. In our experiments across small-to-moderate network sizes, ModEn-Hub-style orchestration maintains substantially higher teleportation success probabilities than the baseline, albeit at the cost of more entanglement generation attempts. These results provide high-level evidence that adaptive, centrally coordinated resource management is a promising architectural pattern for scalable, near-term distributed quantum computing.
Scaling quantum computing beyond a single device requires networking many quantum processing units (QPUs) into a coherent quantum-HPC system. We propose the Modular Entanglement Hub (ModEn-Hub) architecture: a hub-and-spoke photonic interconnect paired with a real-time quantum network orchestrator. ModEn-Hub centralizes entanglement sources and shared quantum memory to deliver on-demand, high-fidelity Bell pairs across heterogeneous QPUs, while the control plane schedules teleportation-based non-local gates, launches parallel entanglement attempts, and maintains a small ebit cache. To quantify benefits, we implement a lightweight, reproducible Monte Carlo study under realistic loss and tight round budgets, comparing a naive sequential baseline to an orchestrated policy with logarithmically scaled parallelism and opportunistic caching. Across 1-128 QPUs and 2,500 trials per point, ModEn-Hub-style orchestration sustains about 90% teleportation success while the baseline degrades toward about 30%, at the cost of higher average entanglement attempts (about 10-12 versus about 3). These results provide clear, high-level evidence that adaptive resource orchestration in the ModEn-Hub enables scalable and efficient quantum-HPC operation on near-term hardware.
Modular quantum computing scales quantum systems by connecting Quantum Processing Units (QPUs) through a quantum network. A key challenge in such architectures is efficiently scheduling entanglement generation to enable distributed circuit execution. This work investigates how architectural factors—such as network topology, switch placement, and communication qubit allocation—impact entanglement scheduling and resource utilization. Through simulation, we evaluate static and dynamic scheduling strategies under realistic constraints including probabilistic entanglement generation, limited communication qubits, reconfiguration delays, and qubit coherence time. Our results show that dynamic scheduling performs better in highly connected architectures, while sparse topologies lead to contention and entanglement loss. We also profile Bell State Measurement (BSM) demand to identify architectural bottlenecks and inform provisioning. These insights highlight the critical role of architecture in enabling scalable, efficient distributed quantum computing.
Realizing distributed architectures for quantum computing is crucial to scaling up computational power. A key component of such architectures is a scheduler that coordinates operations over a short-range quantum network required to enable the necessary non-local entangling gates between quantum processing units (QPUs). It is desirable to determine schedules of minimum make span, which in the case of networks with constrained resources hinges on their efficient usage. Here we compare and contrast two approaches to solving the make span minimization problem, an approach based on the resource constrained project scheduling (RCPSP) framework, and another based on a greedy heuristic algorithm. The workflow considered is as follows. Firstly, the computational circuit is partitioned and assigned to different QPUs such that the number of nonlocal entangling gates acting across partitions is minimized while the qubit load is nearly uniform on the individual QPUs, which can be accomplished using, e.g., the METIS solver. Secondly, the nonlocal entangling gate requirements with respect to the partitions are identified, and mapped to network operation sequences that deliver the necessary entanglement between the QPUs. Finally, the network operations are scheduled such that the make span is minimized. As illustrative examples, we analyze the implementation of a small instance of the Quantum Fourier Transform algorithm over instances of a simple hub and spoke (star) network architecture comprised of a quantum switch as the hub and QPUs as spokes, each with a finite qubit resource budget. In one instance, our results show the RCPSP approach outperforming the greedy heuristic. In another instance, we find the two performing equally well. Our results thus illustrate the effectiveness of the RCPSP framework, while also underlining the relevance and usefulness of greedy heuristics.
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.
Distributed quantum computing (DQC) connects multiple quantum processing units (QPUs) via quantum interconnects to overcome the qubit-count and connectivity limitations of individual devices. A critical challenge in this paradigm is routing entanglement resources efficiently across the quantum interconnect network so that inter-QPU quantum gates can be executed with high fidelity and minimal resource waste. We propose the Entanglement-Aware Adaptive Routing (EAAR) framework, which jointly optimizes path selection, entanglement purification scheduling, and congestion-aware load balancing for DQC workloads. EAAR models the quantum network as a fidelity-weighted directed graph and transforms the maximum-fidelity path problem into an additive shortest-path problem. An adaptive purification scheduler targets bottleneck links when end-to-end fidelity falls below application-defined thresholds, and a dynamic congestion penalty steers traffic away from over-utilized entanglement buffers. We evaluate EAAR in simulation across grid, ring, random-regular, and small-world topologies ranging from 4 to 24 QPU nodes. Results demonstrate that EAAR improves average remote-gate fidelity by up to 10.3% and reduces entanglement consumption by up to 44.8% compared to greedy baselines, while maintaining competitive latency with shortest-path routing. These findings confirm that holistic, entanglement-aware routing is essential for practical, high-fidelity distributed quantum computation.
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.