Distributed quantum computing (DQC) mitigates the hardware limitations of near-term devices by partitioning large circuits into smaller segments executed on qubit-limited processors. However, even these reduced circuits often exceed the capacity of near-term devices that provide only extremely few reliable qubits. To address this, we introduce a qubit-reuse based DQC framework that exploits mid-circuit measurement and reset to enable scalable execution with minimal physical qubits. We consider distributed quantum learning (DQL) as a representative case study and formulate an energy minimization problem for the proposed framework that jointly optimizes quantum operation energy, measurement repetitions, and transmission power. To tackle the inherent non-convexity, we design a hybrid block coordinate descent (BCD) and successive convex approximation (SCA) algorithm for efficient solution. Extensive simulations on MNIST and CIFAR-10 demonstrate that the proposed qubit-reuse framework enables scalable execution on qubit-limited devices while achieving competitive performance compared to centralized quantum computing, and that the joint optimization algorithm significantly improves energy efficiency over baseline schemes.
Distributed quantum computing (DQC) mitigates the hardware limitations of near-term devices by partitioning large circuits into smaller segments executed on qubit-limited processors. However, even these reduced circuits often exceed the capacity of near-term devices that provide only extremely few reliable qubits. To address this, we introduce a qubit-reuse based DQC framework that exploits mid-circuit measurement and reset to enable scalable execution with minimal physical qubits. We consider distributed quantum learning (DQL) as a representative case study and formulate an energy minimization problem for the proposed framework that jointly optimizes quantum operation energy, measurement repetitions, and transmission power. To tackle the inherent non-convexity, we design a hybrid block coordinate descent (BCD) and successive convex approximation (SCA) algorithm for efficient solution. Extensive simulations on MNIST and CIFAR-10 demonstrate that the proposed qubit-reuse framework enables scalable execution on qubit-limited devices while achieving competitive performance compared to centralized quantum computing, and that the joint optimization algorithm significantly improves energy efficiency over baseline schemes.
Abstract To overcome the physical limitations of current quantum hardware in terms of available qubits and connectivity, distributed quantum computing (DQC) has emerged as a promising and scalable paradigm. However, in distributed settings, cross-node qubit interactions incur high communication overhead due to the use of costly quantum communication protocols. Efficient circuit partitioning and transmission cost optimization have thus become key challenges. This work addresses the often-overlooked issues of hardware heterogeneity and redundant transmission by proposing a resource-aware partitioning and transmission cost optimization method for distributed quantum circuits. First, we develop a partitioning framework constrained by qubit resources, which accommodates node capacity differences to enable flexible qubit allocation. Second, we model gate dependencies using a directed acyclic graph (DAG) representation and introduce formal criteria to detect “initial-state” and “final-state” redundancies. A measurement-reset strategy is then employed to replace part of the quantum communication, reducing inter-node data transmission. Experimental results on a variety of benchmark circuits and heterogeneous architectures demonstrate that our method significantly reduces transmission cost and improves overall resource utilization. These findings offer both theoretical insight and practical guidance for efficient distributed quantum computing.
Present quantum computers are constrained by limited qubit capacity and restricted physical connectivity, leading to challenges in large-scale quantum computations. Distributing quantum computations across a network of quantum computers is a promising way to circumvent these challenges and facilitate large quantum computations. However, distributed quantum computations require entanglements (to execute remote gates) which can incur significant generation latency and, thus, lead to decoherence of qubits. In this work, we consider the problem of distributing quantum circuits across a quantum network to minimize the execution time. The problem entails mapping the circuit qubits to network memories, including within each computer since limited connectivity within computers can affect the circuit execution time. We provide two-step solutions for the above problem: In the first step, we allocate qubits to memories to minimize the estimated execution time; for this step, we design an efficient algorithm based on an approximation algorithm for the max-quadratic-assignment problem. In the second step, we determine an efficient execution scheme, including generating required entanglements with minimum latency under the network resource and decoherence constraints; for this step, we develop two algorithms with appropriate performance guarantees under certain settings or assumptions. We consider multiple protocols for executing remote gates, viz., telegates and cat-entanglements. With extensive simulations over NetSquid, a quantum network simulator, we demonstrate the effectiveness of our developed techniques and show that they outperform a scheme based on prior work by 40 to 50% on average and up to 95% in some cases.
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) connects many small quantum processors into a single logical machine, offering a practical route to scalable quantum computation. However, most existing DQC paradigms are structure-agnostic. Circuit cutting proposed by Peng et al. in [Phys. Rev. Lett., Oct. 2020] reduces per-device qubits at the cost of exponential classical post-processing, while search-space partitioning proposed by Avron et al. in [Phys. Rev. A., Nov. 2021] distributes the workload but weakens Grover's ideal quadratic speedup. In this paper, we introduce a structure-aware framework for distributed quantum optimization that resolves this complexity-resource trade-off. We model the objective function as a factor graph and expose its sparse interaction structure. We cut the graph along its natural ``seams'', i.e., a separator of boundary variables, to obtain loosely coupled subproblems that fit on resource-constrained processors. We coordinate these subproblems with shared entanglement, so the network executes a single globally coherent search rather than independent local searches. We prove that this design preserves Grover-like scaling: for a search space of size N, our framework achieves O(N) query complexity up to processors and separator dependent factors, while relaxing the qubit requirement of each processor. We extend the framework with a hierarchical divide-and-conquer strategy that scales to large-scale optimization problems and supports two operating modes: a fully coherent mode for fault-tolerant networks and a hybrid mode that inserts measurements to cap circuit depth on near-term devices. We validate the predicted query-entanglement trade-offs through simulations over diverse network topologies, and we show that structure-aware decomposition delivers a practical path to scalable distributed quantum optimization on quantum networks.
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 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.