Distributed quantum computing (DQC) offers a pathway for scaling up quantum computing architectures beyond the confines of a single chip. Entanglement is a crucial resource for implementing nonlocal operations in DQC, and it is required to allow teleportation of quantum states and gates. Remote entanglement generation in practical systems is probabilistic, has longer duration than that of local operations, and is nondeterministic. Therefore, optimizing the performance of probabilistic remote entanglement generation is critically important for the performance of DQC architectures. In this paper we propose and study a new DQC architecture that combines (1) buffering of successfully generated entanglement, (2) asynchronously attempted entanglement generation, and (3) adaptive scheduling of remote gates based on the entanglement generation pattern. We show that our hardware-software co-design improves both the runtime and the output fidelity under a realistic model of DQC.
Distributed quantum computing (DQC) offers a pathway for scaling up quantum computing architectures beyond the confines of a single chip. Entanglement is a crucial resource for implementing nonlocal operations in DQC, and it is required to allow teleportation of quantum states and gates. Remote entanglement generation in practical systems is probabilistic, has longer duration than that of local operations, and is nondeterministic. Therefore, optimizing the performance of probabilistic remote entanglement generation is critically important for the performance of DQC architectures. In this paper we propose and study a new DQC architecture that combines (1) buffering of successfully generated entanglement, (2) asynchronously attempted entanglement generation, and (3) adaptive scheduling of remote gates based on the entanglement generation pattern. We show that our hardware-software co-design improves both the runtime and the output fidelity under a realistic model of DQC.
Abstract In distributed quantum computing (DQC), quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections. Meanwhile, quantum software tries its best to reduce the required number of remote quantum gates between chips. However, this “hardware first, software follows” methodology may not fully exploit the potential of DQC. Inspired by classical software–hardware co-design, this paper explores the design space of application-specific DQC architectures. More specifically, we propose AutoArch, an automated quantum chip network (QCN) structure design tool. With qubits grouping followed by a customized QCN design, AutoArch can generate a near-optimal DQC architecture suitable for target quantum algorithms. Experimental results show that the DQC architecture generated by AutoArch can outperform other general QCN architectures when executing target quantum algorithms.
In distributed quantum computing (DQC), executing monolithic quantum circuits across multiple interconnected quantum processing units (QPUs) requires dedicated communication qubits to generate and distribute entanglement. Because the number of physical qubits within a QPU is finite, a trade-off emerges where allocating more communication qubits increases the capacity of quantum channels for concurrent non-local operations, but reduces the number of computational qubits available for local gate operations. Distributed quantum compilation routinely ignores this channel capacity, while hardware architects lack a method to determine it prior to quantum circuit partitioning. Moreover, scheduling entanglement on demand introduces severe latency, whereas pre-fetching exposes stored pairs to decoherence. We propose an economic order quantity model from perishable inventory theory to optimize the trade-off between entanglement distribution latency and the time cost of decoherence. The resulting estimate is driven by algorithmic demand and physical constraints, offering a dual application for the hardware-software co-design of high-performance DQC: for hardware architects, it gives the optimal allocation of dedicated communication qubits in static heterogeneous architectures; for compiler developers, it gives the optimal number to reserve dynamically in homogeneous architectures.
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.
Quantum computing promises revolutionary advancements across various fields, but its practical realization hinges on overcoming significant challenges in algorithm execution. Traditional hardware-software co-design approaches for quantum computing often rely on pre-defined architectures and compilation strategies, which can be suboptimal for diverse quantum algorithms. This paper proposes an adaptive hardware-software co-design framework that dynamically adjusts the quantum computing system's configuration – encompassing both hardware architecture and software compilation – in response to the specific requirements of the algorithm being executed. The core claim is that optimized co-design is pivotal for efficient quantum algorithm execution. This framework leverages a dynamic optimization loop, continuously monitoring algorithm performance and adjusting parameters such as qubit connectivity, gate set selection, and compilation strategies. The system utilizes a mathematical model to represent the algorithm's computational demands and hardware capabilities, enabling real-time adaptation. This approach moves beyond static design methodologies, offering a pathway towards significantly improved performance and resource utilization in quantum computing systems. The framework's ability to adapt in real-time represents a crucial step towards unlocking the full potential of quantum algorithms.
Fault tolerant quantum computation over distributed quantum computing (DQC) platforms requires careful evaluation of resource requirements and noise thresholds. As quantum hardware advances toward modular and networked architectures, various fault tolerant DQC schemes have been proposed, which can be broadly categorized into three architectural types. Type 1 architectures consist of small quantum nodes connected via Greenberger-Horne-Zeilinger (GHZ) states, enabling nonlocal stabilizer measurements. Type 2 architectures distribute a large error correcting code block across multiple modules, with most stabilizer measurements remaining local, except for a small subset at patch boundaries that are performed using nonlocal CNOT gates. Type 3 architectures assign code blocks to distinct modules and can perform fault tolerant operations such as transversal gates, lattice surgery, and teleportation to implement logical operations between code blocks. Using the planar surface code and toric code as representative examples, we analyze how the resource requirements, particularly the number of Bell pairs and the average number of generation attempts, scale with increasing code distance across different architectural designs. This analysis provides valuable insights for identifying architectures well suited to fault tolerant distributed quantum computation under near term hardware and resource constraints.
Distributed quantum computing (DQC) is a scalable way to build a large-scale quantum computing system. Previous compilers for DQC focus on either qubit-to-qubit inter-node gates or qubit-to-node nonlocal circuit blocks, missing opportunities of optimizing collective communication which consists of nonlocal gates over multiple nodes. In this paper, we observe that by utilizing patterns of collective communication, we can greatly reduce the amount of inter-node communication required to implement a group of nonlocal gates. We propose QuComm, the first compiler framework which unveils and analyzes collective communication patterns hidden in distributed quantum programs and efficiently routes inter-node gates on any DQC architecture based on discovered patterns, cutting down the overall communication cost of the target program. We also provide the first formalization of the communication buffer concept in DQC compiling. The communication buffer utilizes data qubits to store remote entanglement so that we can ensure enough communication resources on any DQC architecture to support the proposed optimizations for collective communication. Experimental results show that, compared to the state-of-the-art baseline, QuComm reduces the amount of inter-node communication by 54.9% on average, over various distributed quantum programs and DQC hardware configurations.