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 |
| Published: |
American Physical Society
2021-08-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.3.033135 |
| Summary: | 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. |
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| ISSN: | 2643-1564 |