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Quantifying quantum entanglement via a hybrid quantum-classical machine learning framework

Xiaodie Lin1, Zhenyu Chen1, and Zhaohui Wei2,3,*

  • 1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
  • 2Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China
  • 3Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing 101407, China

  • *weizhaohui@gmail.com
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Phys. Rev. A 107, 062409 – Published 9 June, 2023

DOI: https://doi.org/10.1103/PhysRevA.107.062409

Abstract

Quantifying entanglement for quantum states whose density matrices are unknown is a difficult task, but also becomes more and more necessary because of the fast development of quantum engineering. Machine learning provides practical solutions to this fundamental problem, where one has to train a proper machine learning model to predict entanglement measures of unknown quantum states based on experimentally measurable data, say, moments of density matrices or correlation data produced by local measurements. In this paper, we compare the performance of these two different machine learning approaches systematically. Particularly, we first show that the approach based on moments enjoys a remarkable advantage over that based on correlation data, though the cost of measuring moments is much higher. Next, since correlation data is much easier to obtain experimentally, we try to better its performance by proposing a hybrid quantum-classical machine learning framework for this problem, where the key is to train optimal local measurements, or even optimal tensor products of single-qubit observables, to generate more informative correlation data. Our numerical simulations show that the new hybrid framework brings us comparable performance with the approach based on moments to quantify entanglement, and furthermore, it turns out that the new hybrid framework has a nice noise-resistant capability when handling realistic correlation data.

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Information

Phys. Rev. A 107, 062409– Published 9 June, 2023
  • Received 16 January 2023
  • Accepted 26 May 2023
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