AI 中文总结
针对现有量子控制平台功能覆盖不全的问题，提出QCORE量子处理器端数字控制参考架构，通过四硬件分区与多机制协同，在延迟、误差控制、扩展能力等方面表现优异。
AI 中文摘要
可扩展的量子处理器需要控制、读出、反馈、校准和纠错功能在有限延迟与共享资源约束下协同运作，而现有平台通常仅针对这些能力的子集进行优化。本文提出QCORE（面向量子控制的实时执行架构）——一种位于主机与平台专属模拟/混合信号前端之间的量子处理器（QPU）端数字控制参考架构。QCORE将任务管理、共享资源、硬实时执行和长时标服务划分为四个硬件分区：快速结果边带实现同轮反馈闭环，测量报文（Measurement Packet）提供可追溯的测量与服务接口，通用服务控制框架、Tile本地量子纠错（QEC）以及带版本的安全点提交机制则统筹校准、纠错与长期状态更新。研究采用事务级、事件驱动和量子行为模型开展评估：在0.8的背景负载下，共享测量报文/事件反馈路径的P99延迟为(1.984±0.004)L_max；闭环运行将平均频率误差降低83.2%±0.8%，并将最大读出漂移下的状态分配误差从10.39%±0.54%降至5.37%±0.29%；在10万次配置事务中未观测到不安全接受或混合版本现象，Tile本地QEC降低了建模的全局边界需求，带来2.08倍的容量归一化扩展估算值。
英文摘要
Scalable quantum processors require control, readout, feedback, calibration, and error correction to coexist under bounded latency and shared-resource constraints, whereas existing platforms typically optimize only a subset of these capabilities. This article presents QCORE (Quantum-Control-Oriented Real-Time Execution), a QPU-side digital control reference architecture positioned between the Host and a platform-specific analog/mixed-signal front end. QCORE separates task management, shared resources, hard-real-time execution, and long-timescale services into four hardware partitions. A fast-result sideband closes same-round feedback, a Measurement Packet provides a traceable measurement and service interface, and a common service-control skeleton, Tile-local QEC, and versioned safe-point commit organize calibration, error correction, and long-term state updates. Transaction-level, event-driven, and quantum-behavioral models are used for evaluation. At a background load of 0.8, the $P_{99}$ latency of the shared Measurement Packet/Event feedback path is $(1.984\pm0.004)L_{\max}$. Closed-loop operation reduces the mean frequency error by $83.2\%\pm0.8\%$ and lowers the state-assignment error at maximum readout drift from $10.39\%\pm0.54\%$ to $5.37\%\pm0.29\%$. No unsafe acceptance or mixed-version observation is observed in 100,000 configuration transactions, and Tile-local QEC reduces modeled global-boundary demand and yields a $2.08\times$ capacity-normalized scaling estimate.
Comments12 pages, 13 figures
