Quantum entanglement recognition

• URL: http://arxiv.org/abs/2007.14397v2
• Date: Mon, 12 Apr 2021 18:15:35 GMT
• Title: Quantum entanglement recognition
• Authors: Jun Yong Khoo and Markus Heyl
• Abstract summary: We formulate a framework for probing entanglement based on machine learning techniques. We show that the resulting quantum entanglement recognition task is accurate and can be assigned a well-controlled error.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entanglement constitutes a key characteristic feature of quantum matter. Its detection, however, still faces major challenges. In this letter, 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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