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....

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Veröffentlicht in:Applied physics letters 2021-09, Vol.119 (11)
Hauptverfasser: Navarathna, Rohit, Jones, Tyler, Moghaddam, Tina, Kulikov, Anatoly, Beriwal, Rohit, Jerger, Markus, Pakkiam, Prasanna, Fedorov, Arkady
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Sprache:eng
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Zusammenfassung: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 classifications. 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.
ISSN:0003-6951
1077-3118
DOI:10.1063/5.0065011