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

• URL: http://arxiv.org/abs/2107.05857v2
• Date: Wed, 17 Nov 2021 07:20:45 GMT
• Title: Neural networks for on-the-fly single-shot state classification
• Authors: Rohit Navarathna, Tyler Jones, Tina Moghaddam, Anatoly Kulikov, Rohit Beriwal, Markus Jerger, Prasanna Pakkiam and Arkady Fedorov
• Abstract summary: We investigate the application of neural networks to state classification in a single-shot quantum measurement. Our method is ready for on-the-fly data processing without overhead or need for large data transfer to a hard drive.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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