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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 |
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Main Authors: | , , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
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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. |
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ISSN: | 2331-8422 |
DOI: | 10.48550/arxiv.2305.02956 |