Neural Networks for Detecting Multimode Wigner Negativity

Valeria Cimini, Marco Barbieri, Nicolas Treps, Mattia Walschaers, and Valentina Parigi
Phys. Rev. Lett. 125, 160504 – Published 16 October 2020
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Abstract

The characterization of quantum features in large Hilbert spaces is a crucial requirement for testing quantum protocols. In the continuous variable encoding, quantum homodyne tomography requires an amount of measurement that increases exponentially with the number of involved modes, which practically makes the protocol intractable even with few modes. Here, we introduce a new technique, based on a machine learning protocol with artificial neural networks, that allows us to directly detect negativity of the Wigner function for multimode quantum states. We test the procedure on a whole class of numerically simulated multimode quantum states for which the Wigner function is known analytically. We demonstrate that the method is fast, accurate, and more robust than conventional methods when limited amounts of data are available. Moreover, the method is applied to an experimental multimode quantum state, for which an additional test of resilience to losses is carried out.

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  • Received 9 March 2020
  • Revised 20 July 2020
  • Accepted 16 September 2020

DOI:https://doi.org/10.1103/PhysRevLett.125.160504

© 2020 American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & TechnologyGeneral Physics

Authors & Affiliations

Valeria Cimini1,*, Marco Barbieri1, Nicolas Treps2, Mattia Walschaers2, and Valentina Parigi2

  • 1Dipartimento di Scienze, Università degli Studi Roma Tre, Via della Vasca Navale 84, 00146 Rome, Italy
  • 2Laboratoire Kastler Brossel, Sorbonne Université, CNRS, ENS-PSL Research University, Collège de France, 4 place Jussieu, F-75252 Paris, France

  • *valeria.cimini@uniroma3.it

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Issue

Vol. 125, Iss. 16 — 16 October 2020

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