将神经网络嵌入量子读出硬件，实现高精度低延迟的量子比特读取。
End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml
- 在QICK硬件上部署量化训练的神经网络，直接运行于FPGA
- 单次测量保真度达96%，延迟仅32纳秒，资源占用低于16%
- 适合量子计算研究者快速构建机器学习驱动的读出系统
我们提出一种端到端的超导量子比特读出工作流，将协同设计的神经网络嵌入量子仪器控制套件（QICK）。基于基于Xilinx RFSoC FPGA的定制固件与软件，该工作流利用hls4ml工具包，通过量化解析训练，将机器学习模型转化为高效的FPGA硬件实现，采用友好的Python API。实验验证了单个横模量子比特读出的算法设计、优化与集成，实现了96%的单次测量保真度，延迟为32纳秒，FPGA查找表资源利用率不足16%。结果为社区提供了一条可访问的路径，以推进机器学习驱动的读出与自适应控制在量子信息处理中的应用。
原文摘要 · Abstract (English)
We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes the hls4ml package and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python APIs. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.
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