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Exploring large-scale entanglement in quantum simulation.

Manoj K JoshiChristian KokailRick van BijnenFlorian KranzlTorsten V ZacheRainer BlattChristian F RoosPeter Zoller
Published in: Nature (2023)
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 1 . Here we perform experimental investigations of entanglement on the basis of the entanglement Hamiltonian (EH) 2 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 3 and perform sample-efficient 'learning' of the EH for subsystems of up to 20 lattice sites 4 . 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 5,6 , 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 2 . Our results also show the transition from area- to volume-law scaling 7 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.
Keyphrases
  • molecular dynamics
  • energy transfer
  • monte carlo
  • deep learning
  • quantum dots
  • social media
  • neural network