Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910292387037184 |
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| author | 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. |
| author_facet | 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. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_02956 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator 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. Quantum Physics Disordered Systems and Neural Networks 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. |
| title | Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator |
| topic | Quantum Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2305.02956 |