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Published October 22, 2021 | Accepted Version + Published + Supplemental Material
Journal Article Open

Quantum Variational Learning of the Entanglement Hamiltonian

Abstract

Learning the structure of the entanglement Hamiltonian (EH) is central to characterizing quantum many-body states in analog quantum simulation. We describe a protocol where spatial deformations of the many-body Hamiltonian, physically realized on the quantum device, serve as an efficient variational ansatz for a local EH. Optimal variational parameters are determined in a feedback loop, involving quench dynamics with the deformed Hamiltonian as a quantum processing step, and classical optimization. We simulate the protocol for the ground state of Fermi-Hubbard models in quasi-1D geometries, finding excellent agreement of the EH with Bisognano-Wichmann predictions. Subsequent on-device spectroscopy enables a direct measurement of the entanglement spectrum, which we illustrate for a Fermi Hubbard model in a topological phase.

Additional Information

© 2021 American Physical Society. Received 12 May 2021; revised 20 August 2021; accepted 1 September 2021; published 22 October 2021. We thank L. K. Joshi, R. Kaubrügger, J. Carrasco, J. Yu, and B. Kraus for valuable discussions. We thank Ana Maria Rey and Murray Holland for a careful reading of the manuscript. We acknowledge funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 817482 (Pasquans) and No. 731473 (QuantERA via QT-FLAG). Furthermore, this work was supported by the Simons Collaboration on Ultra-Quantum Matter, which is a grant from the Simons Foundation (651440, P. Z.), and LASCEM by AFOSR No. 64896-PH-QC. M. D. is partly supported by the ERC under Grant No. 758329 (AGEnTh). A. E. acknowledges funding by the German National Academy of Sciences Leopoldina under the Grant No. LPDS 2021-02. B. V. acknowledges funding from the Austrian Science Foundation (FWF, P 32597 N), and the French National Research Agency (ANR-20-CE47-0005, JCJC project QRand). The computational results presented here have been achieved (in part) using the LEO HPC infrastructure of the University of Innsbruck.

Attached Files

Published - PhysRevLett.127.170501.pdf

Accepted Version - 2105.04317.pdf

Supplemental Material - SM.pdf

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Additional details

Created:
August 20, 2023
Modified:
October 23, 2023