Quantum entanglement recognition

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.

10 pages, 10 figures

References in corpus (5)

• QuTiP 2: A Python framework for the dynamics of open quantum systems
• The ITensor Software Library for Tensor Network Calculations
• Measuring entanglement growth in quench dynamics of bosons in an optical lattice
• Measuring entanglement entropy of a generic many-body system with a quantum switch
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Cited by in corpus (2)

• Quantifying Unknown Quantum Entanglement via a Hybrid Quantum-Classical Machine Learning Framework
• Certification of quantum states with hidden structure of their bitstrings