The growing demand for large-scale quantum computers is pushing research on Distributed Quantum Computing (DQC). Recent experimental efforts have demonstrated some of the building blocks for such a design. DQC systems are clusters of quantum processing units (QPUs) connected by means of quantum network infrastructures. Their extension ranges from the single box to the geographical scale. Furthermore, they can be integrated with classical High Performance Computing systems. Simulation modeling of DQC architectures provides a safe way to test and explore different what-if scenarios. Many simulation tools have been developed to support the research community in designing and evaluating quantum computer and quantum network technologies, including hardware, protocols, and applications. However, a framework for DQC simulation putting equal emphasis on computational and networking aspects has never been proposed, so far. In this paper, a design framework for DQC simulation is presented, whose core component is an Execution Manager that schedules DQC jobs for running on networked quantum computers. Two metrics are proposed for evaluating the impact of the job scheduling algorithms with respect to QPU utilization and quantum network utilization, beyond the traditional concept of makespan. The discussion is supported by a DQC job scheduling example, where two different strategies are compared in terms of the proposed metrics.
The growing demand for large-scale quantum computers is pushing research on Distributed Quantum Computing (DQC). Recent experimental efforts have demonstrated some of the building blocks for such a design. DQC systems are clusters of quantum processing units (QPUs) connected by means of quantum network infrastructures. Their extension ranges from the single box to the geographical scale. Furthermore, they can be integrated with classical High Performance Computing systems. Simulation modeling of DQC architectures provides a safe way to test and explore different what-if scenarios. Many simulation tools have been developed to support the research community in designing and evaluating quantum computer and quantum network technologies, including hardware, protocols, and applications. However, a framework for DQC simulation putting equal emphasis on computational and networking aspects has never been proposed, so far. In this paper, a design framework for DQC simulation is presented, whose core component is an Execution Manager that schedules DQC jobs for running on networked quantum computers. Two metrics are proposed for evaluating the impact of the job scheduling algorithms with respect to QPU utilization and quantum network utilization, beyond the traditional concept of makespan. The discussion is supported by a DQC job scheduling example, where two different strategies are compared in terms of the proposed metrics.
Distributed quantum computing (DQC) is being actively investigated as a means of scaling the number of qubits across multiple connected quantum devices. This includes quantum circuit compilation and execution management on multiple quantum devices in the network. The latter aspect is very challenging because, while reducing the makespan of job batches remains a relevant objective, novel quantum-specific constraints must be considered, including QPU utilization, non-local gate rate, and the latency associated with queued DQC jobs. In this work, a range of scheduling strategies is proposed, simulated, and evaluated, including heuristics that prioritize resource maximization for QPU utilization, node selection based on heterogeneous network connectivity, asynchronous node release upon job completion, and a scheduling strategy based on reinforcement learning with proximal policy optimization. These approaches are benchmarked against traditional FIFO and LIST schedulers under varying DQC job types and network conditions for the allocation of DQC jobs to devices within a network.
We describe the development of the NetQStack Simulator, a network-integrated simulation framework designed to evaluate distributed quantum computing (DQC) within data center environments. Our approach strictly models the network topology, classical control plane, and quantum data plane required to execute distributed quantum workloads. The simulator models classical and physical-layer protocol synchronization, schedules sub-circuit executions on individual Quantum Processing Units (QPUs), and routes entanglement generation requests across Bell State Measurement (BSM) nodes. With the ability to use external network-aware circuit partitioning software, and interface with an intelligent quantum network controller, this architecture enables researchers to execute fully distributed algorithms while rigorously evaluating the impact of network latency, routing heuristics, and control-plane overhead. Our early results demonstrate that a network-integrated approach provides useful insights when assessing the performance and scalability of distributed quantum algorithms.
Distributed quantum computing (DQC) that allows a large quantum circuit to be executed simultaneously on multiple quantum processing units (QPUs) becomes a promising approach to increase the scalability of quantum computing. It is natural to envision the near-future DQC platform as a multi-tenant cluster of QPUs, called a Quantum Cloud. However, no existing DQC work has addressed the two key problems of running DQC in a multi-tenant quantum cloud: placing multiple quantum circuits to QPUs and scheduling network resources to complete these jobs. This work is the first attempt to design a circuit placement and resource scheduling framework for a multi-tenant environment. The proposed framework is called CloudQC, which includes two main functional components, circuit placement and network scheduler, with the objectives of optimizing both quantum network cost and quantum computing time. Experimental results with real quantum circuit workloads show that CloudQC significantly reduces the average job completion time compared to existing DQC placement algorithms for both single-circuit and multi-circuit DQC. We envision this work will motivate more future work on network-aware quantum cloud.
In Distributed Quantum Computing (DQC), quantum networks interconnect multiple Quantum Processing Units (QPUs) to overcome single-device limitations and enable cross-server computation. We present QTest, a modular simulation testbed for distributed quantum computing and networking. The platform employs Deep Reinforcement Learning (DRL)-based quantum circuit partitioning and distributed quantum network scheduling, coordinating cross-server operations under network constraints such as routing and channel utilization. With containerized QPU nodes and GPU-accelerated backends, QTest supports circuit execution across heterogeneous and geographically distributed environments. By enabling task partitioning, inter-node entanglement distribution, fidelity-aware routing, and latency-throughput evaluation, QTest provides a reproducible and extensible environment for protocol development and validation. Experimental results show that QTest achieves efficient runtime scalability under ideal conditions. Compared to CPU-based execution, GPU-accelerated distributed runs reduce execution time by up to an order of magnitude, validating the effectiveness of the proposed testbed.
The challenge of scaling quantum computers to gain computational power is expected to lead to architectures with multiple connected quantum processing units (QPUs), commonly referred to as Distributed Quantum Computing (DQC). In parallel, there is a growing momentum toward treating quantum computers as accelerators, integrating them into the heterogeneous architectures of high-performance computing (HPC) environments. This work combines these two foreseeable futures in CUNQA, an open-source DQC emulator designed for HPC environments that allows testing, evaluating and studying DQC in HPC before it even becomes real. It implements the three DQC models of no-communication, classical-communication and quantum-communication; which will be examined in this work. Addressing programming considerations, explaining emulation and simulation details, and delving into the specifics of the implementation will be part of the effort. The well-known Quantum Phase Estimation (QPE) algorithm is used to demonstrate and analyze the emulation of the models. To the best of our knowledge, CUNQA is the first tool designed to emulate the three DQC schemes in an HPC environment.
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.