AI 中文总结
研究分布式量子计算中通信量子比特与计算量子比特的资源权衡问题，提出基于易腐库存理论的经济订货量模型，可为硬件架构师和编译器开发者在不同架构中进行资源最优分配提供方法。
AI 中文摘要
在分布式量子计算（DQC）中，跨多个互连量子处理单元（QPU）执行整体量子电路需要专用通信量子比特来生成和分配纠缠。由于QPU内物理量子比特数量有限，存在权衡：分配更多通信量子比特增加并发非局部操作的量子通道容量，但减少用于本地门操作的计算量子比特数量。分布式量子编译通常忽略此通道容量，硬件架构师缺乏在量子电路分区前确定它的方法。此外，按需调度纠缠会引入严重延迟，而预取会使存储的对暴露于退相干。我们提出基于易腐库存理论的经济订货量模型，以优化纠缠分布延迟和退相干时间成本之间的权衡。结果估计由算法需求和物理约束驱动，为高性能DQC的软硬件协同设计提供双重应用：对硬件架构师，它给出静态异构架构中专用通信量子比特的最优分配；对编译器开发者，它给出同构架构中动态预留的最优数量。
英文摘要
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.
Comments4 pages, 1 figure, 1 table; accepted at the Workshop on DQC-SI: Distributed Quantum Computing Systems and Infrastructure, IEEE QCE 2026
