Neural networks for on-the-fly single-shot state classification
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.

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Rohit Navarathna completed his MSc in Physics from the Birla Institute of Technology and Science, Pilani. His master’s thesis titled "3D Supercondcting Transmon Qubit" was at a superconducting devices lab in the Indian Institute of Science, Bangalore.

The thesis was intriguing enough that he continued to work in this lab for another year fabricating and testing transmon and Josephson Parametric devices. In October 2018, he joined SQDLab as a PhD student to continue learning about superconducting quantum circuits.