Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator
Abstract
Supervised quantum learning is an emergent multidisciplinary domain bridging between variational quantum algorithms and classical machine learning. Here, we study experimentally a hybrid classifier model accelerated by a quantum simulator - a linear array of four superconducting transmon artificial atoms - trained to solve multilabel classification and image recognition problems. We train a quantum circuit on simple binary and multi-label tasks, achieving classification accuracy around 95%, and a hybrid model with data re-uploading with accuracy around 90% when recognizing handwritten decimal digits. Finally, we analyze the inference time in experimental conditions and compare the performance of the studied quantum model with known classical solutions.
Cite
@article{arxiv.2305.02956,
title = {Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator},
author = {Aleksei Tolstobrov and Gleb Fedorov and Shtefan Sanduleanu and Shamil Kadyrmetov and Andrei Vasenin and Aleksey Bolgar and Daria Kalacheva and Viktor Lubsanov and Aleksandr Dorogov and Julia Zotova and Peter Shlykov and Aleksei Dmitriev and Konstantin Tikhonov and Oleg V. Astafiev},
journal= {arXiv preprint arXiv:2305.02956},
year = {2024}
}
Comments
11 pages, 6 figures