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

• URL: http://arxiv.org/abs/2305.02956v2
• Date: Wed, 10 Jan 2024 08:25:23 GMT
• Title: Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator
• Authors: Aleksei Tolstobrov, Gleb Fedorov, Shtefan Sanduleanu, Shamil Kadyrmetov, Andrei Vasenin, Aleksey Bolgar, Daria Kalacheva, Viktor Lubsanov, Aleksandr Dorogov, Julia Zotova, Peter Shlykov, Aleksei Dmitriev, Konstantin Tikhonov, Oleg V. Astafiev
• Abstract summary: Supervised quantum learning is an emergent multidisciplinary domain bridging between variational quantum algorithms and classical machine learning. 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.
• Score: 29.81784450632149
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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