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

TL;DR

This paper demonstrates that neural networks can be effectively used for real-time, single-shot quantum state classification, improving fidelity and robustness against experimental imperfections in superconducting qubit measurements.

Contribution

It introduces a neural network-based method for on-the-fly quantum state classification that enhances fidelity and handles experimental imperfections without data transfer overhead.

Findings

Increased assignment fidelity for two and three state classification.

Method enables real-time data processing without large data transfer.

Neural networks trained to be robust against experimental imperfections.

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.