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) |
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Hauptverfasser: | , , , , , , , |
Format: | Artikel |
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. |
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ISSN: | 0003-6951 1077-3118 |
DOI: | 10.1063/5.0065011 |