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.
Neural networks for on-the-fly single-shot state classification
Note: This paper is part of the APL Special Collection on Emerging Qubit Systems - Novel Materials, Encodings and Architectures.
Rohit Navarathna, Tyler Jones, Tina Moghaddam, Anatoly Kulikov, Rohit Beriwal, Markus Jerger, Prasanna Pakkiam, Arkady Fedorov; Neural networks for on-the-fly single-shot state classification. Appl. Phys. Lett. 13 September 2021; 119 (11): 114003. https://doi.org/10.1063/5.0065011
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