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Optimal State Transfer and Entanglement Generation in Power-Law Interacting Systems.
Tran, Minh C; Guo, Andrew Y; Deshpande, Abhinav; Lucas, Andrew; Gorshkov, Alexey V.
Afiliação
  • Tran MC; Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, Maryland 20742, USA.
  • Guo AY; Joint Quantum Institute, NIST/University of Maryland, College Park, Maryland 20742, USA.
  • Deshpande A; Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, Maryland 20742, USA.
  • Lucas A; Joint Quantum Institute, NIST/University of Maryland, College Park, Maryland 20742, USA.
  • Gorshkov AV; Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, Maryland 20742, USA.
Phys Rev X ; 11(3)2021 Jul.
Article em En | MEDLINE | ID: mdl-37551271
We present an optimal protocol for encoding an unknown qubit state into a multiqubit Greenberger-Horne-Zeilinger-like state and, consequently, transferring quantum information in large systems exhibiting power-law (1/rα) interactions. For all power-law exponents α between d and 2d+1, where d is the dimension of the system, the protocol yields a polynomial speed-up for α>2d and a superpolynomial speed-up for α≤2d, compared to the state of the art. For all α>d, the protocol saturates the Lieb-Robinson bounds (up to subpolynomial corrections), thereby establishing the optimality of the protocol and the tightness of the bounds in this regime. The protocol has a wide range of applications, including in quantum sensing, quantum computing, and preparation of topologically ordered states. In addition, the protocol provides a lower bound on the gate count in digital simulations of power-law interacting systems.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Phys Rev X Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Phys Rev X Ano de publicação: 2021 Tipo de documento: Article