MagiCFirm：一种算法-硬件协同设计的魔法态培育运行时

中文摘要 AI 辅助

魔法态培育为降低容错量子计算（FTQC）中非克利福德操作的成本提供了一种有前景的替代方案。然而，在实践中实现培育暴露了两个挑战：（i）缺乏介于逻辑软件与物理控制之间的开源经典运行时层，以及（ii）决定魔法态就绪性的协议特定决策的延迟约束。为应对这些挑战，我们采用算法-硬件协同设计方法。在算法层面，我们开发了一种两阶段早期逃逸方案，该方案离线识别信息丰富的探测器子集，构建紧凑的译码问题，并在运行时并行执行部分译码与完整译码，从而允许高置信度的尝试在完整译码完成之前推进。在硬件层面，我们提出了MagiCFirm，一种可配置的运行时，它将离线编译的微程序与用于探测器构建、事件处理和协议控制的专用数据路径相结合，实现魔法态培育的端到端执行。在所评估的配置中，MagiCFirm在匹配逻辑错误率下将墙钟魔法态制备时间最多减少39.3%。对于典型的魔法态受限工作负载，这相当于整体应用运行时间估计减少11%。

英文摘要

Magic-state cultivation offers a promising alternative for lowering the cost of non-Clifford operations in fault-tolerant quantum computing (FTQC). However, realizing cultivation in practice exposes two challenges: (i) the lack of an open-source classical runtime layer between logical software and physical control, and (ii) the latency constraints on protocol-specific decisions that determine magic-state readiness. To address these challenges, we adopt an algorithm--hardware co-design approach. At the algorithm level, we develop a two-stage early-escape scheme that identifies an informative subset of detectors offline, constructs a compact decoding problem, and performs partial decoding in parallel with complete decoding at runtime, allowing high-confidence attempts to advance before full decoding completes. At the hardware level, we present MagiCFirm, a configurable runtime that combines offline-compiled microprograms with dedicated datapaths for detector construction, event processing, and protocol control, enabling end-to-end execution of magic-state cultivation. Across evaluated configurations, MagiCFirm reduces wall-clock magic-state preparation time by up to 39.3% at matched logical error rate. For a representative magic-state-bound workload, this translates to an estimated 11% reduction in overall application runtime.

发表机构

• Imperial College London（帝国理工学院）
• University of Cambridge（剑桥大学）

机构由 AI 辅助整理，请以论文原文为准。