Distributed quantum computation is a promising approach to building a fault-tolerant quantum computer at practical scale. To complete program execution within the minimum time, computational resources and communication resources should be assigned to reduce communication cost. Since remote lattice surgery operation between spatially separated quantum computers takes longer than local lattice surgery, minimizing delay due to the remote lattice surgery is a crucial issue to improve performance of distributed quantum computation. This delay is driven by three related issues: assignment of variable qubits to specific nodes; placement of those qubits within the nodes (resulting in contention for the limited number of local qubit movement paths); and contention for inter-node communication resources (both network interface communication qubits and channels) during concurrent operations. Focusing on the second problem, we propose placing qubits near the network interface based on their inter-node communication needs to avoid intra-node path collisions. We evaluate execution time of the VBE ripple-carry adder in a$2 \times 2$grid topology, comparing our proposed qubit assignment approach to random placement. Our results suggest that the proposed model has the potential to reduce resource contention and to improve total execution time.
Distributed quantum computation is a promising approach to building a fault-tolerant quantum computer at practical scale. To complete program execution within the minimum time, computational resources and communication resources should be assigned to reduce communication cost. Since remote lattice surgery operation between spatially separated quantum computers takes longer than local lattice surgery, minimizing delay due to the remote lattice surgery is a crucial issue to improve performance of distributed quantum computation. This delay is driven by three related issues: assignment of variable qubits to specific nodes; placement of those qubits within the nodes (resulting in contention for the limited number of local qubit movement paths); and contention for inter-node communication resources (both network interface communication qubits and channels) during concurrent operations. Focusing on the second problem, we propose placing qubits near the network interface based on their inter-node communication needs to avoid intra-node path collisions. We evaluate execution time of the VBE ripple-carry adder in a2×2grid topology, comparing our proposed qubit assignment approach to random placement. Our results suggest that the proposed model has the potential to reduce resource contention and to improve total execution time.
Practical fault-tolerant quantum computation requires a large number of physical qubits, posing a major challenge for scaling up individual quantum processors. Distributed architectures offer a promising solution by interconnecting multiple processors through quantum networks. However, remote Bell-pair generation remains technically demanding, making it essential to assess computational performance under limited communication resources. In this work, we present a comprehensive resource analysis of distributed fault-tolerant quantum computation using superconducting processors connected by microwave or optical links. We show that even a single physical communication channel per node can substantially reduce the physical-qubit requirements per node under experimentally motivated, forward-looking microwave-link assumptions of a 1 MHz physical Bell-pair generation rate and 90% fidelity, although at the cost of a roughly tenfold increase in execution time. Conversely, reducing the execution-time overhead requires substantially greater communication resources, including higher Bell-pair fidelities and extensive multiplexing. These results demonstrate the potential feasibility of distributed fault-tolerant quantum computation with limited communication resources and provide a quantitative basis for balancing quantum-link performance, processor size, and execution time.
Distributed quantum circuit simulation enables the execution of large-scale quantum algorithms by partitioning qubits across multiple computational nodes. However, inefficient qubit placement frequently leads to excessive inter-node communication, severely limiting performance and scalability. This work introduces a communication-aware qubit layout optimization framework that systematically minimizes data exchange among nodes while dynamically selecting the optimal number of computational resources. Integrated into the QuCSLO library, our approach supports dynamic node scaling, OpenQASM parsing, and seamless integration with the QuEST simulator. Extensive evaluations on both synthetic benchmarks and real-world QASMBench circuits demonstrate that optimized layouts significantly reduce communication cuts and yield runtime speedups of over 2x compared to identity layouts, particularly in highly entangled circuits. Our results underscore the critical impact of strategic qubit placement on distributed simulation efficiency and provide a practical, resource-scalable solution for quantum workloads.
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.
Encoding logical qubits with surface codes and performing multi-qubit logical operations with lattice surgery is one of the most promising approaches to demonstrate fault-tolerant quantum computing. Thus, a method to efficiently schedule a sequence of lattice-surgery operations is vital for high-performance fault-tolerant quantum computing. A possible strategy to improve the throughput of lattice-surgery operations is splitting a large instruction into several small instructions, such as Bell state preparation and measurements, and executing a part of them in advance. However, scheduling methods to fully utilize this idea have yet to be explored. In this paper, we propose a fast and high-performance scheduling algorithm for lattice-surgery instructions leveraging this strategy. We achieved this by converting the scheduling problem of lattice-surgery instructions to a graph problem of embedding 3D paths into a 3D lattice, which enables us to explore efficient scheduling by solving path search problems in the 3D lattice. Based on this reduction, we propose a method to solve the path-finding problems, the look-ahead Dijkstra projection. We numerically show that this method reduced the execution time of benchmark programs generated from quantum phase estimation algorithms by 3.8 times compared with a naive method based on greedy algorithms. Our study establishes the relation between the lattice-surgery scheduling and graph search problems, which leads to further theoretical analysis on compiler optimization of fault-tolerant quantum computing.
Distributed quantum computing can potentially address the scalability challenge by networking processors through photon-mediated remote entanglement. Prior approaches assumed that remote Bell pairs require distillation before use, incurring substantial overhead, to achieve sufficiently high fidelity. However, recent results show that lattice-surgery operations at logical qubit boundaries tolerate significantly higher error rates than previously assumed. We quantify the resource trade-offs between distillation overhead and surface-code distance requirements under realistic constraints including probabilistic entanglement generation and memory decoherence. We identify the fidelity crossover point separating the two regimes. Below this threshold, the distillation strategy dominates, reducing resource overhead by up to two orders of magnitude. Above it, no-distillation becomes the more efficient choice, reducing resource overhead by more than half. We briefly describe the application of these methods to ion-trap and neutral-atom platforms. These results provide joint design guidelines for optimizing photonic interconnects and fault-tolerant architectures in distributed quantum computing.
Distributing quantum circuits across a network of quantum devices is a recent approach to improve the scalability of quantum computation. Entangled pairs of qubits distributed across network nodes are used as a communication resource for executing inter-node (remote) gates. However, establishing these entanglement pairs incurs significant latency, and doing so during the circuit's execution may lead to the decoherence of logical qubits. Instead, it may be beneficial to distribute certain entanglement pairs before the circuit is executed. Thus, we consider the problem of distributing a quantum circuit across a quantum network containing some pre-distributed entanglement pairs and seek to minimize the execution time of the circuit.
We address this problem in three steps. First, given the quantum network and (optionally) a probability distribution over training circuits, we determine a set of pre-distributed entanglement pairs under a specified budget. Second, we compute a qubit-to-memory allocation that minimizes the expected execution time given these pre-distributed resources. Finally, we identify any additional entanglement pairs required during execution, generate them concurrently, and execute the circuit gates. We evaluate our methods on a broad range of benchmark circuits.