Quantum entanglement recognition
Entanglement constitutes a key characteristic feature of quantum matter. Its detection, however, still faces major challenges. In this paper, we formulate a framework for probing entanglement based on machine learning techniques. The central element is a protocol for the generation of statistical im...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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American Physical Society
2021-08-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.3.033135 |
| _version_ | 1827285706699964416 |
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| author | Jun Yong Khoo Markus Heyl |
| author_facet | Jun Yong Khoo Markus Heyl |
| author_sort | Jun Yong Khoo |
| collection | DOAJ |
| description | Entanglement constitutes a key characteristic feature of quantum matter. Its detection, however, still faces major challenges. In this paper, we formulate a framework for probing entanglement based on machine learning techniques. The central element is a protocol for the generation of statistical images from quantum many-body states, with which we perform image classification by means of convolutional neural networks. We show that the resulting quantum entanglement recognition task is accurate and can be assigned a well-controlled error across a wide range of quantum states. We discuss the potential use of our scheme to quantify quantum entanglement in experiments. Our developed scheme provides a generally applicable strategy for quantum entanglement recognition in both equilibrium and nonequilibrium quantum matter. |
| first_indexed | 2024-04-24T10:18:12Z |
| format | Article |
| id | doaj.art-394bf199676f47cbbc1a030b9623d737 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:18:12Z |
| publishDate | 2021-08-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-394bf199676f47cbbc1a030b9623d7372024-04-12T17:12:39ZengAmerican Physical SocietyPhysical Review Research2643-15642021-08-013303313510.1103/PhysRevResearch.3.033135Quantum entanglement recognitionJun Yong KhooMarkus HeylEntanglement constitutes a key characteristic feature of quantum matter. Its detection, however, still faces major challenges. In this paper, we formulate a framework for probing entanglement based on machine learning techniques. The central element is a protocol for the generation of statistical images from quantum many-body states, with which we perform image classification by means of convolutional neural networks. We show that the resulting quantum entanglement recognition task is accurate and can be assigned a well-controlled error across a wide range of quantum states. We discuss the potential use of our scheme to quantify quantum entanglement in experiments. Our developed scheme provides a generally applicable strategy for quantum entanglement recognition in both equilibrium and nonequilibrium quantum matter.http://doi.org/10.1103/PhysRevResearch.3.033135 |
| spellingShingle | Jun Yong Khoo Markus Heyl Quantum entanglement recognition Physical Review Research |
| title | Quantum entanglement recognition |
| title_full | Quantum entanglement recognition |
| title_fullStr | Quantum entanglement recognition |
| title_full_unstemmed | Quantum entanglement recognition |
| title_short | Quantum entanglement recognition |
| title_sort | quantum entanglement recognition |
| url | http://doi.org/10.1103/PhysRevResearch.3.033135 |
| work_keys_str_mv | AT junyongkhoo quantumentanglementrecognition AT markusheyl quantumentanglementrecognition |