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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2204.04198 |
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| _version_ | 1866915698915147776 |
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| author | Dawid, Anna Arnold, Julian Requena, Borja Gresch, Alexander Płodzień, Marcin Donatella, Kaelan Nicoli, Kim A. Stornati, Paolo Koch, Rouven Büttner, Miriam Okuła, Robert Muñoz-Gil, Gorka Vargas-Hernández, Rodrigo A. Cervera-Lierta, Alba Carrasquilla, Juan Dunjko, Vedran Gabrié, Marylou Huembeli, Patrick van Nieuwenburg, Evert Vicentini, Filippo Wang, Lei Wetzel, Sebastian J. Carleo, Giuseppe Greplová, Eliška Krems, Roman Marquardt, Florian Tomza, Michał Lewenstein, Maciej Dauphin, Alexandre |
| author_facet | Dawid, Anna Arnold, Julian Requena, Borja Gresch, Alexander Płodzień, Marcin Donatella, Kaelan Nicoli, Kim A. Stornati, Paolo Koch, Rouven Büttner, Miriam Okuła, Robert Muñoz-Gil, Gorka Vargas-Hernández, Rodrigo A. Cervera-Lierta, Alba Carrasquilla, Juan Dunjko, Vedran Gabrié, Marylou Huembeli, Patrick van Nieuwenburg, Evert Vicentini, Filippo Wang, Lei Wetzel, Sebastian J. Carleo, Giuseppe Greplová, Eliška Krems, Roman Marquardt, Florian Tomza, Michał Lewenstein, Maciej Dauphin, Alexandre |
| contents | In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_04198 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Modern applications of machine learning in quantum sciences Dawid, Anna Arnold, Julian Requena, Borja Gresch, Alexander Płodzień, Marcin Donatella, Kaelan Nicoli, Kim A. Stornati, Paolo Koch, Rouven Büttner, Miriam Okuła, Robert Muñoz-Gil, Gorka Vargas-Hernández, Rodrigo A. Cervera-Lierta, Alba Carrasquilla, Juan Dunjko, Vedran Gabrié, Marylou Huembeli, Patrick van Nieuwenburg, Evert Vicentini, Filippo Wang, Lei Wetzel, Sebastian J. Carleo, Giuseppe Greplová, Eliška Krems, Roman Marquardt, Florian Tomza, Michał Lewenstein, Maciej Dauphin, Alexandre Quantum Physics Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning. |
| title | Modern applications of machine learning in quantum sciences |
| topic | Quantum Physics Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2204.04198 |