In Distributed Quantum Computing (DQC), reliable entanglement distribution across a network of quantum computers is crucial. During the quantum program compilation process, the timing and locations of required entanglement resources can be determined. Leveraging this information, we use the properties of quantum network hardware-specifically, the entanglement generation and quantum switch operation rates-to optimize quantum job execution schedules. This work presents a system that, given a set of quantum programs, extracts an Entaglement Demand Schedule (EDS) and generates an execution plan, including resource allocation tables and switch control instructions. We outline the system architecture and benchmark its performance using collections of randomized quantum programs, and describe further steps to enhance our system.
In Distributed Quantum Computing (DQC), reliable entanglement distribution across a network of quantum computers is crucial. During the quantum program compilation process, the timing and locations of required entanglement resources can be determined. Leveraging this information, we use the properties of quantum network hardware-specifically, the entanglement generation and quantum switch operation rates-to optimize quantum job execution schedules. This work presents a system that, given a set of quantum programs, extracts an Entaglement Demand Schedule (EDS) and generates an execution plan, including resource allocation tables and switch control instructions. We outline the system architecture and benchmark its performance using collections of randomized quantum programs, and describe further steps to enhance our system.
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.
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.
Quantum computing continues to face challenges in scaling both qubit quality and quantity, motivating the shift toward Distributed Quantum Computing (DQC), where multiple interconnected Quantum Processing Units (QPUs) collaboratively solve problems too complex for monolithic systems. In a quantum data center, QPUs distributed across racks can perform remote gate operations by creating entanglement between them, enabling circuit execution through DQC. However, generating end-to-end entangled pairs in a quantum data center often causes congestion and resource contention. To mitigate this, we introduce a resource management framework with efficient schedulers that maximize fidelity-guaranteed throughput while preserving the dependency constraints of sub-circuits derived from the original quantum circuit. We benchmark the scheduling problem using a MixedInteger Linear Programming (MILP) model and then propose a set of lightweight schedulers-Least Resource First (LRF) and its extension, Group LRF (GLRF). These lightweight schedulers provide practical, though less optimal, solutions for handling the high volume of remote gates in DQC workloads. Our initial results on a DCell-based quantum data center show that such heuristics perform well under current DQC conditions, balancing runtime and performance while avoiding the heavy overhead of solvers or machine learning approaches, which often struggle to meet the strict timing demands of short-lived entanglement.
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.
With a growing number of quantum networks in operation, there is a pressing need for performance analysis of quantum switching technologies. A quantum switch establishes, distributes, and maintains entanglements across a network. In contrast to a classical switching fabric, a quantum switch is a two-sided queueing network. The switch generates Link-Level Entanglements (LLEs) across links that connect users to the switch, which are then fused to process the network's entanglement requests. First, we characterize the capacity region that is defined as the set of entanglement request rates for which there exists a scheduling policy stabilizing the system. We then show that a sequence of Max-Weight policies that we propose achieve throughput optimality in the asymptotic sense. Our proof techniques analyse a two-time scale separation phenomenon at the fluid scale for a general switch topology. This allows us to demonstrate that the optimal fluid dynamics are given by a scheduling algorithm that solves a certain average reward Markov Decision Process.
Emerging quantum networks enable distributed quantum computing (DQC) by interconnecting modest, noisy quantum processors. However, practical deployments face tight memory budgets, probabilistic entanglement generation, and decoherence, all while serving multiple users. In realistic settings -- cloud execution, iterative/interactive workloads and concurrent jobs -- circuits do not arrive in a single batch but as a stream. This creates a fundamentally online problem: the system must react to stochastic arrivals, balance latency and throughput, and remain stable under varying loads without foresight of future jobs.
We study Online Scheduling and Distribution of Quantum Circuits (OS-DQC) and formalize the online scheduling problem for quantum networks: circuits arrive over time and must be executed without knowledge of future arrivals. At each event time (arrival or completion), the system decides which waiting circuits to launch and how to place their qubits, while accounting for congestion from in-flight circuits and constraints due to finite memories and decoherence. The objective is to minimize the mean completion time and makespan under these physical and resource constraints.