RushHour：一种动态可重构的格手术架构
RushHour: A Dynamically Reconfigurable Lattice-Surgery Architecture
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中文总结 AI 辅助
RushHour通过硬件-编译器协同设计实现动态格手术，解决现有格手术的静态局限，在小型芯片适配、运行速度等方面显著优于现有方案，为容错量子计算提供高效架构。
中文摘要 AI 辅助
实用的容错量子计算（FTQC）需要高效的格手术（LS），以便大型算法能适配资源受限的量子芯片。然而现有方法存在局限性：量子比特、布线空间和资源态在执行前就已分配，这导致无法在小型芯片上运行，静态调度的执行存在较大时间开销，且每种设计固定在时空权衡的单一区域。我们提出动态LS，它能实现辅助空间的高效重构、资源态的即时分配以及逻辑量子比特的动态旋转，从而用单一统一方法覆盖整个时空权衡范围。我们通过硬件-编译器协同设计，用RushHour实现动态LS：RushHour指令集架构（ISA）形式化并编程我们的动态格模型；格管理单元抽象动态格管理并执行高效格重构；RushHour编译器将逻辑电路编译为物理芯片的优化ISA程序，同时对指令进行流水线处理。我们将RushHour与六种最先进的编译器和两种资源模型进行对比评估：在最小型芯片上，86%的基准测试仅能通过RushHour运行，而现有方法需要1.2至3.5倍大的芯片；在空间受限的早期FTQC芯片上，RushHour的运行速度比最佳可行替代方案快2.0至7.2倍，同时在超大型芯片上达到与最先进水平相当的结果；RushHour的构造性结果相比理想化机器的资源限制，仅超出4.8倍。
英文摘要
Practical fault-tolerant quantum computing (FTQC) requires efficient lattice surgery (LS), so that large algorithms fit on resource-constrained quantum chips. Existing approaches, however, are rigid: qubits, routing space, and resource states are allocated ahead of execution, which prevents running on small chips, leaves statically scheduled executions with large time overheads, and fixes each design at a single area of the space-time trade-off. We present dynamic LS, which enables efficient reconfiguration of the ancilla space, just-in-time allocation of resource states, and dynamic rotations of logical qubits, thereby spanning the entire space-time trade-off with a single, unified approach. We realize dynamic LS with RushHour through a hardware-compiler co-design: the RushHour ISA formalizes and programs our dynamic lattice model, the Lattice Management Unit abstracts dynamic lattice management and performs efficient lattice reconfiguration, and the RushHour Compiler compiles logical circuits for physical chips into optimized ISA programs while pipelining instructions. We evaluate RushHour against six state-of-the-art compilers and two resource models. On the smallest chips, 86% of benchmarks run only with RushHour, while existing approaches require 1.2-3.5$\times$ larger chips. On space-constrained early-FTQC chips, RushHour runs a median 2.3-7.2$\times$ faster than the best feasible alternative, while achieving results comparable to the state of the art on very large chips. RushHour's constructive results run 4.8$\times$ from an idealized-machine resource limit.
