AI 中文总结
研究针对量子控制处理器依赖定制指令集、可扩展性差等问题，提出基于RISC-V向量引擎的矢量化量子控制方法，能高效处理多量子比特，支持动态调整与测量，经评估在执行时间上比基线加速2.52倍，扩展性良好。
AI 中文摘要
量子控制处理器（QCP）弥合了编译器工具链与控制电子设备之间的差距，负责将编译后的量子电路转换为可直接操纵量子比特并处理测量反馈的可执行指令。然而，现有设计主要依赖定制指令集，限制了设计复用且构建支持工具链需大量精力。此外，在高度可扩展场景中有效寻址量子比特和调度操作仍是关键挑战。本文提出一种基于RISC-V向量（RVV）引擎并带有面向量子扩展的矢量化量子控制方法。利用RVV的高并行性，该方法能在单条指令中处理多达128个量子比特。还将参数化旋转信息嵌入指令集，支持混合量子-经典程序中门旋转的动态调整。为支持电路中的测量，设计了基于硬件的暂停-恢复协议，能在接收测量结果80纳秒内恢复流水线执行。使用RISC-V工具链和FPGA原型进行的综合评估表明，该设计在程序执行时间上比基线实现了高达2.52倍的加速，具有出色的可扩展性。
英文摘要
The Quantum Control Processor (QCP) bridges the gap between compiler toolchains and control electronics, and is responsible for translating compiled quantum circuits into executable instructions that directly manipulate qubits and handle measurement feedback. However, existing designs rely primarily on customized instruction sets, limiting design reuse and requiring significant effort to build supporting toolchains. Furthermore, efficiently addressing qubits and scheduling operations in highly scalable scenarios remains a critical challenge. In this work, we present a vectorized quantum control approach built upon the RISC-V Vector (RVV) engine with a quantum-oriented extension. Leveraging the high parallelism of RVV, our approach can address up to 128 qubits in a single instruction. We also embed parameterized rotation information into the instruction set, enabling dynamic tuning of gate rotations in hybrid quantum-classical programs. To support mid-circuit measurements, we design a hardware-based halt-resume protocol that resumes pipeline execution within 80 $ns$ of receiving the measurement result. Comprehensive evaluation using both RISC-V toolchains and FPGA prototypes demonstrates that our design achieves up to 2.52$\times$ speedup over the baseline in program execution time, with excellent scalability.
CommentsAccepted at IEEE International Conference on Quantum Computing and Engineering (QCE) 2026
