Neural networks for on-the-fly single-shot state classification
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.
Cite
@article{arxiv.2107.05857,
title = {Neural networks for on-the-fly single-shot state classification},
author = {Rohit Navarathna and Tyler Jones and Tina Moghaddam and Anatoly Kulikov and Rohit Beriwal and Markus Jerger and Prasanna Pakkiam and Arkady Fedorov},
journal= {arXiv preprint arXiv:2107.05857},
year = {2023}
}