Distributed Quantum Computing (DQC) expands qubit capacity by interconnecting multiple Quantum Processing Units (QPUs), but remote gate execution introduces significant entanglement overhead. In this paper, we study the Remote Gate Scheduling problem in DQC (RGS-DQC) under a hybrid Telegate and Teledata model, provide a formal formulation, and establish its NP hardness. To address this challenge, we proposeLABUBU, a layer buffered bundled optimization framework that integrates coordinate wise pruned greedy refinement with bounded perturbation under QPU capacity constraints while maintaining linear complexity per iteration. Extensive simulations on both structured Quantum Fourier Transform circuits and unstructured random circuits show that Labubu consistently reduces entanglement cost compared with Telegate-SA, Telegate-RD, Teledata-ZS, and the competitive GateCover baseline. Experiments on QEC encoded circuits further confirm its potential for large scale fault tolerant distributed quantum computing.
Distributed Quantum Computing (DQC) expands qubit capacity by interconnecting multiple Quantum Processing Units (QPUs), but remote gate execution introduces significant entanglement overhead. In this paper, we study the Remote Gate Scheduling problem in DQC (RGS-DQC) under a hybrid Telegate and Teledata model, provide a formal formulation, and establish its NP hardness. To address this challenge, we proposeLABUBU, a layer buffered bundled optimization framework that integrates coordinate wise pruned greedy refinement with bounded perturbation under QPU capacity constraints while maintaining linear complexity per iteration. Extensive simulations on both structured Quantum Fourier Transform circuits and unstructured random circuits show that Labubu consistently reduces entanglement cost compared with Telegate-SA, Telegate-RD, Teledata-ZS, and the competitive GateCover baseline. Experiments on QEC encoded circuits further confirm its potential for large scale fault tolerant distributed quantum computing.
Quantum computing has the potential to outperform classical computing in solving specific problems. However, the limited qubit capacity of existing Quantum Processing Units (QPUs) poses significant barriers to the practical implementation of quantum computing. Distributed quantum computing (DQC) offers a promising approach to scaling the qubit capacity of quantum systems by interconnecting multiple QPUs and enabling collaborative computation. Nevertheless, DQC necessitates implementing remote quantum gate operations that consume entangled qubit pairs, which poses a significant challenge for DQC. In this work, we formulate and investigate the remote gate scheduling (RGS) problem, considering two approaches for remote gate operations: Telegate and Teledata. We propose a hybrid heuristic algorithm that dynamically schedules quantum gate operations within a circuit, executed on distributed QPUs, while minimizing entanglement consumption. We conducted extensive simulations using real-world quantum circuits and processors to evaluate the proposed approach. The results show that our approach reduces entanglement consumption by up to 90% and 25% compared to the two baselines, Telegate-SA and Teledata-ZS, respectively. Furthermore, the execution time of our approach is significantly shorter than that of the baselines.
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.
Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs), but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.
Distributed quantum computing (DQC) enables scalable quantum computations by distributing large quantum circuits on multiple quantum processing units (QPUs) in the quantum cloud. In DQC, after partitioning quantum circuits, they must be scheduled and executed on heterogenous QPUs while balancing latency, overhead, QPU communication resource limits. However, since fully functioning quantum communication networks have not been realized yet, near-term quantum clouds will only rely on local operations and classical communication settings between QPUs, without entangled quantum links. Additionally, existing DQC scheduling frameworks do not account for user-defined execution deadlines and adopt inefficient wire cutting techniques. Accordingly, in this work, a deadline aware DQC scheduling framework with efficient wire cutting for near-term quantum cloud is proposed. The proposed framework schedules partitioned quantum subcircuits while accounting for circuit deadlines and QPU capacity limits. It also captures dependencies between partitioned subcircuits and distributes the execution of the sampling shots on different QPUs to have efficient wire cutting and faster execution. In this regard, a deadline-aware circuit scheduling optimization problem is formulated, and solved using simulated annealing. Simulation results show a marked improvement over existing shot-agnostic frameworks under urgent deadlines, reaching a 12.8% increase in requests served before their deadlines. Additionally, the proposed framework serves 8.16% more requests, on average, compared to state-of-the-art dependency-agnostic baseline frameworks, and by 9.60% versus the dependency-and-shot-agnostic baseline, all while achieving a smaller makespan of the DQC execution. Moreover, the proposed framework serves 23.7%, 24.5%, and 25.38% more requests compared to greedy, list scheduling, and random schedulers, respectively.
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) 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.