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arXiv · 2107.05857

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

Rohit Navarathna·Tyler Jones·Tina Moghaddam·Anatoly Kulikov·Rohit Beriwal·Markus Jerger·Prasanna Pakkiam·Arkady Fedorov
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Abstract

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, Tyler Jones, Tina Moghaddam, Anatoly Kulikov, Rohit Beriwal, Markus Jerger, Prasanna Pakkiam, Arkady Fedorov. 2021-07-13. Neural networks for on-the-fly single-shot state classification. https://doi.org/10.1063/5.0065011

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