English(EN) Spiking neural networks for streaming qubit readout

脉冲神经网络增强实时量子比特读出

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 17:40

研究人员开发了一种使用脉冲神经网络（SNN）的新方法，用于在量子处理器中更快、更准确地分配量子比特状态。这些SNN实时处理测量数据，在获取读出信号时提供不断变化的分类分数。这种流式处理能力优于传统的匹配滤波技术，并且在准确性上接近全轨迹人工神经网络，有潜力在FPGA硬件上实现低延迟、实时的量子比特读出。 AI

影响这项研究通过提高量子比特状态测量的速度和准确性，有望实现更高效、响应更快的量子计算机控制系统。

排序理由该条目是一篇arXiv预印本，详细介绍了脉冲神经网络在特定科学问题中的新应用。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Benjamin Lienhard · 2026-10-01 17:40

用于流式量子比特读出的脉冲神经网络

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…

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用于流式量子比特读出的脉冲神经网络

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