APL paper files

Neural networks have proven to be efficient for a number of practical applications ranging from image recognition to identifying phase transitions in quantum physics models. In this paper we investigate the application of neural networks to state classification in a single-shot quantum measurement. We use dispersive readout of a superconducting transmon circuit to demonstrate an increase in assignment fidelity for both two and three state classification. More importantly, our method is ready for on-the-fly data processing without overhead or need for large data transfer to a hard drive. In addition we demonstrate the capacity of neural networks to be trained against experimental imperfections, such as phase drift of a local oscillator in a heterodyne detection scheme.神经网络已被证实可高效应用于诸多实际场景，涵盖图像识别乃至量子物理模型中的相变识别。本文研究了神经网络在单次量子测量中的态分类应用。我们通过超导传输子（transmon）电路的色散读出方案，证实了该方法可提升二态与三态分类的赋值保真度。更为关键的是，本方法可实现无额外开销的实时数据处理，无需将大量数据传输至硬盘。此外，我们还验证了神经网络可针对实验非理想因素进行训练，例如外差探测方案中本地振荡器的相位漂移。