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Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator

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...

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Published in:arXiv.org 2024-01
Main Authors: Tolstobrov, Aleksei, Fedorov, Gleb, Sanduleanu, Shtefan, Kadyrmetov, Shamil, Vasenin, Andrei, Bolgar, Aleksey, Kalacheva, Daria, Lubsanov, Viktor, Dorogov, Aleksandr, Zotova, Julia, Shlykov, Peter, Dmitriev, Aleksei, Tikhonov, Konstantin, Astafiev, Oleg V
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Language:English
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2305.02956