发表机构
Ulster University; Technical University of Munich; Walther-Meißner-Institut, Bayerische Akademie der Wissenschaften; Munich Center for Quantum Science and Technology; Zurich Instruments（阿尔斯特大学; 慕尼黑工业大学; 瓦尔特·迈斯纳研究所，巴伐利亚科学院; 慕尼黑量子科学与技术中心; 苏黎世仪器）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出用脉冲神经网络（SNN）判别器进行超导量子比特的流式读出，通过分块处理测量窗口实现低延迟分类，性能优于匹配滤波并接近人工神经网络，且能在FPGA上实时更新，为低延迟量子控制提供新途径。
AI 中文摘要
快速且准确的量子比特状态分配对于量子处理器中的反馈、校准和纠错至关重要。在超导平台上，频分复用读出使得这一任务本质上具有多变量特性，因为测量迹线可能编码串扰、量子比特态弛豫事件以及其他瞬态非理想性，而这些无法被传统匹配滤波完全捕获。在此，我们引入用于超导量子比特读出的脉冲神经网络（SNN）判别器。通过将测量窗口划分为连续的时间块处理，网络利用时间结构并在数据到达时更新分类分数，而非等待读出窗口结束。脉冲网络优于匹配滤波判别，并接近全迹人工神经网络的精度。除了达到人工神经网络的性能外，SNN的关键优势在于它们提供随读出信号获取而演化的流式、时间分辨的量子比特态估计。利用量化感知训练和hls4ml综合，我们进一步证明每个FPGA推理更新可在下一个读出块到达前完成。这些结果确立了脉冲神经网络作为在FPGA硬件上实现低延迟、实时量子比特读出的有前景途径，并对时间关键的量子控制和科学推理应用具有更广泛的意义。
英文摘要
Fast and accurate qubit-state assignment is essential for feedback, calibration, and error correction in quantum processors. In superconducting platforms, frequency-multiplexed readout makes this task intrinsically multivariate as measured traces can encode crosstalk, qubit-state relaxation events, and other transient nonidealities that are not fully captured by conventional matched filtering. Here, we introduce spiking neural network (SNN) discriminators for superconducting qubit readout. By processing the measurement window in successive time chunks, the networks exploit temporal structure and update classification scores as data arrive, rather than waiting until the end of the readout window. The spiking networks outperform matched-filter discrimination and approach the accuracy of a full-trace artificial neural network. Beyond reaching the performance of artificial neural networks, the key advantage of SNNs is that they provide a streaming, time-resolved estimate of the qubit state that evolves as the readout signal is acquired. Using quantisation-aware training and hls4ml synthesis, we further demonstrate that each FPGA inference update can be completed before the next readout chunk arrives. These results establish spiking neural networks as a promising route to low-latency, real-time qubit readout on FPGA hardware, with broader implications for time-critical quantum-control and scientific-inference applications.
Comments25 pages, 9 figures, 6 tables
