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Exploring large-scale entanglement in quantum simulation.
- Source :
-
Nature [Nature] 2023 Dec; Vol. 624 (7992), pp. 539-544. Date of Electronic Publication: 2023 Nov 29. - Publication Year :
- 2023
-
Abstract
- Entanglement is a distinguishing feature of quantum many-body systems, and uncovering the entanglement structure for large particle numbers in quantum simulation experiments is a fundamental challenge in quantum information science <superscript>1</superscript> . Here we perform experimental investigations of entanglement on the basis of the entanglement Hamiltonian (EH) <superscript>2</superscript> as an effective description of the reduced density operator for large subsystems. We prepare ground and excited states of a one-dimensional XXZ Heisenberg chain on a 51-ion programmable quantum simulator <superscript>3</superscript> and perform sample-efficient 'learning' of the EH for subsystems of up to 20 lattice sites <superscript>4</superscript> . Our experiments provide compelling evidence for a local structure of the EH. To our knowledge, this observation marks the first instance of confirming the fundamental predictions of quantum field theory by Bisognano and Wichmann <superscript>5,6</superscript> , adapted to lattice models that represent correlated quantum matter. The reduced state takes the form of a Gibbs ensemble, with a spatially varying temperature profile as a signature of entanglement <superscript>2</superscript> . Our results also show the transition from area- to volume-law scaling <superscript>7</superscript> of von Neumann entanglement entropies from ground to excited states. As we venture towards achieving quantum advantage, we anticipate that our findings and methods have wide-ranging applicability to revealing and understanding entanglement in many-body problems with local interactions including higher spatial dimensions.<br /> (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)
Details
- Language :
- English
- ISSN :
- 1476-4687
- Volume :
- 624
- Issue :
- 7992
- Database :
- MEDLINE
- Journal :
- Nature
- Publication Type :
- Academic Journal
- Accession number :
- 38030731
- Full Text :
- https://doi.org/10.1038/s41586-023-06768-0