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Quantum entanglement recognition

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Khoo,  Jun Yong
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

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Heyl,  Markus
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

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2007.14397.pdf
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Citation

Khoo, J. Y., & Heyl, M. (2021). Quantum entanglement recognition. Physical Review Research, 3(3): 033135. doi:10.1103/PhysRevResearch.3.033135.


Cite as: https://hdl.handle.net/21.11116/0000-0009-466A-2
Abstract
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.