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Variational quantum algorithms for nonlinear problems

Research output: Contribution to journalArticlepeer-review

  • Michael Lubasch
  • Dr Jae Woo Joo
  • Pierre Moinier
  • Martin Kiffner
  • Dieter Jaksch
We show that nonlinear problems including nonlinear partial differential equations can be efficiently solved by variational quantum computing. We achieve this by utilizing multiple copies of variational quantum states to treat nonlinearities efficiently and by introducing tensor networks as a programming paradigm. The key concepts of the algorithm are demonstrated for the nonlinear Schrödinger equation as a canonical example. We numerically show that the variational quantum ansatz can be exponentially more efficient than matrix product states and present experimental proof-of-principle results obtained on an IBM Q device.
Original languageEnglish
Article number010301(R)
JournalPhysical Review A
Volume101
Issue number1
DOIs
Publication statusPublished - 6 Jan 2020

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  • Variational quantum algorithms for nonlinear problems

    Rights statement: Michael Lubasch, Jaewoo Joo, Pierre Moinier, Martin Kiffner, and Dieter Jaksch (2020). 'Variational quantum algorithms for nonlinear problems'. Physical Review A. 101, 010301(R). DOI: 10.1103/PhysRevA.101.010301. © 2020 American Physical Society.

    Final published version, 431 KB, PDF document

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