In addition to the need for stable and precisely controllable qubits, quantum computers take advantage of good readout schemes. Superconducting qubit states can be inferred from the readout signal transmitted through a dispersively coupled resonator. This work proposes a novel readout classification method for superconducting qubits based on a neural network pre-trained with an autoencoder approach. A neural network is pre-trained with qubit readout signals as autoencoders in order to extract relevant features from the data set. Afterwards, the pre-trained network inner layer values are used to perform a classification of the inputs in a supervised manner. We demonstrate that this method can enhance classification performance, particularly for short and long time measurements where more traditional methods present lower performance.
@article{arxiv.2212.00080,
title = {Enhancing Qubit Readout with Autoencoders},
author = {Piero Luchi and Paolo E. Trevisanutto and Alessandro Roggero and Jonathan L. DuBois and Yaniv J. Rosen and Francesco Turro and Valentina Amitrano and Francesco Pederiva},
journal= {arXiv preprint arXiv:2212.00080},
year = {2023}
}