Weak measurements of a superconducting qubit produce noisy voltage signals that are weakly correlated with the qubit state. To recover individual quantum trajectories from these noisy signals, traditional methods require slow qubit dynamics and substantial prior information in the form of calibration experiments. Monitoring rapid qubit dynamics, e.g., during quantum gates, requires more complicated methods with increased demand for prior information. Here, we experimentally demonstrate an alternative method for accurately tracking rapidly driven superconducting qubit trajectories that uses a long short-term memory (LSTM) artificial neural network with minimal prior information. Despite few training assumptions, the LSTM produces trajectories that include qubit-readout resonator correlations due to a finite detection bandwidth. In addition to revealing rotated measurement eigenstates and a reduced measurement rate in agreement with theory for a fixed drive, the trained LSTM also correctly reconstructs evolution for an unknown drive with rapid modulation. Our work enables new applications of weak measurements with faster or initially unknown qubit dynamics, such as the diagnosis of coherent errors in quantum gates.

中文翻译：

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使用神经网络监测快速超导量子比特动力学

超导量子位的弱测量会产生与量子位状态弱相关的噪声电压信号。为了从这些噪声信号中恢复单个量子轨迹，传统方法需要缓慢的量子比特动力学和校准实验形式的大量先验信息。监测快速量子比特动态，例如在量子门期间，需要更复杂的方法，同时对先验信息的需求增加。在这里，我们通过实验展示了一种替代方法，用于准确跟踪快速驱动的超导量子比特轨迹，该方法使用具有最少先验信息的长短期记忆 (LSTM) 人工神经网络。尽管很少有训练假设，但由于检测带宽有限，LSTM 产生的轨迹包括量子位读出谐振器相关性。除了揭示与固定驱动理论一致的旋转测量本征态和降低的测量速率外，经过训练的 LSTM 还通过快速调制正确地重建了未知驱动的演化。我们的工作使弱测量具有更快或最初未知的量子比特动力学的新应用成为可能，例如诊断量子门中的相干误差。