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 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.
As quantum computing transitions into the Noisy Intermediate-Scale Quantum (NISQ) era, scaling monolithic quantum processors remains a significant challenge due to decoherence and operational noise. This dissertation proposes enhancing the connectivity of distributed quantum systems as a scalable solution, focusing on three major research tasks: architectural co-design, operation scheduling optimization, and network topology design. First, to address the physical qubit limits of single-chip processors, we investigate the co-design of network topology and logical qubit allocation for Distributed Quantum Computing (DQC). By modeling qubit interaction as a graph partition problem, we propose a joint optimization framework to minimize inter-QPU communication overhead. Our results demonstrate that the proposed linearized formulation, TACO-L, significantly outperforms traditional two-stage methods in executing complex quantum circuits. Second, we address the challenge of maintaining high-fidelity long-distance entanglements through the optimization of Swapping and Purification Schemes (SPS). We prove that in Binary systems, any optimal swapping and purification tree (OSPT) must follow a Restricted SPT (RT) pattern, where purification is integrated at the link level prior to swapping. Furthermore, we introduce the gradient-based GradTree algorithm, which enables an efficient search for low-cost, high-fidelity scheduling in complex noise environments. Finally, this dissertation formulates the first general Topology Design Problem (TDP) for quantum networks, jointly optimizing quantum memory allocation, optical channel deployment, and entanglement scheduling. By analyzing flow-based and path-based formulations, we prove the optimality of the Relaxed Complete Swapping Tree (RCST) in minimizing the expected cost of entanglement distribution. Collectively, these contributions provide a comprehensive theoretical and algorithmic framework for building scalable, cost-effective global quantum internet architectures.
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) 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.
Distributed quantum computing (DQC) has emerged as a promising approach to overcome the scalability limitations of monolithic quantum processors in terms of computational capability. However, realising the full potential of DQC requires effective resource management and circuit scheduling. This involves efficiently assigning each circuit to a subset of quantum processing units (QPUs), based on factors such as their computational power and connectivity. In heterogeneous DQC networks with arbitrary connectivity topologies and non-identical QPUs, this becomes a complex challenge. This paper addresses resource management and circuit scheduling in such settings, with a focus on computing resource allocation in a quantum data centre. We propose circuit scheduling algorithms based on Mixed-Integer Linear Programming (MILP). Our MILP model accounts for errors arising from inter-QPU communication. In particular, the proposed schemes consider key factors, including network topology, QPU capacities, and quantum circuit structure, to make efficient scheduling and allocation decisions. Simulation results demonstrate that our proposed algorithms significantly improve circuit execution time and scheduling efficiency (measured by makespan and throughput), while also reducing inter-QPU communication overhead, compared to baseline strategies. This work provides valuable insights into resource management strategies for scalable and heterogeneous DQC systems.
Distributed Quantum Computing (DQC) enables scalable quantum execution by interconnecting multiple quantum processing units (QPUs) through quantum networks. In DQC, end-to-end performance is jointly affected by circuit partitioning, entanglement routing, scheduling, and heterogeneous hardware characteristics. However, existing studies often optimize these components independently, providing limited understanding of their cross-layer interactions. In this paper, we present a cross-layer joint-optimization study for DQC using the previously developed SimDisQ-Net simulator. Through simulations, we find that circuit orchestration is one of the dominant factors affecting distributed execution quality, while network-layer mechanisms provide secondary but still meaningful improvements. We further demonstrate that traditional communication metrics, such as hop count or remote-gate count alone, are insufficient predictors of execution quality due to the strong interaction among path fidelity, hardware characteristics, and circuit structure. Motivated by these findings, we propose a topology-aware fidelity proxy (TAFP) evaluation approach that approximates distributed execution fidelity, enabling efficient evaluation of candidate circuit optimizations without time-consuming simulation. Our results highlight the importance of integrated circuit-network orchestration for scalable DQC.
Distributed Quantum Computing (DQC) has the potential to solve industrial large-scale problems by connecting multiple small quantum processors together to form a larger computing system. Concerning the emerging distributed paradigm, a pivotal challenge lies in crafting specialized network topologies to establish efficient connections among quantum processors while minimizing communication costs. In this paper, we propose a novel DQC topology generation algorithm (DQC-TG) to create optimal and near-optimal network topologies for homogeneous and heterogeneous quantum computers, respectively. Furthermore, for specific quantum circuits requiring diverse communication demands between each pair of qubits, we extend the original algorithm into DQC- TG- Plus to design network topologies tailored for these circuits to further enhance the performance. We perform extensive simulations to evaluate the superiority of the generated network topology designs by our algorithms to baselines.