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

Navarathna, Rohit ; Jones, Tyler ; Moghaddam, Tina ; Kulikov, Anatoly ; Beriwal, Rohit ; Jerger, Markus ; Pakkiam, Prasanna ; Fedorov, Arkady

Applied physics letters, 2021-09, Vol.119 (11) [Revista revisada por pares]

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Título:
Neural networks for on-the-fly single-shot state classification
Autor: Navarathna, Rohit ; Jones, Tyler ; Moghaddam, Tina ; Kulikov, Anatoly ; Beriwal, Rohit ; Jerger, Markus ; Pakkiam, Prasanna ; Fedorov, Arkady
Materias: Circuits ; Classification ; Data processing ; Data transfer (computers) ; Neural networks ; Object recognition ; Phase transitions ; Quantum theory
Es parte de: Applied physics letters, 2021-09, Vol.119 (11)
Descripción: 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.
Editor: Melville: American Institute of Physics
Idioma: Inglés

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