Literature DB >> 28949719

Correlation-Enhanced Algorithmic Cooling.

Nayeli A Rodríguez-Briones1,2,3, Eduardo Martín-Martínez1,2,3,4, Achim Kempf1,2,3,4, Raymond Laflamme1,2,3,4.   

Abstract

We propose a method for increasing the purity of interacting quantum systems that takes advantage of correlations present due to the internal interaction. In particular, when this interaction is sufficiently strong, we show that by using the system's quantum correlations one can achieve cooling beyond established limits of previous conventional algorithmic cooling proposals which assume no interaction.

Year:  2017        PMID: 28949719     DOI: 10.1103/PhysRevLett.119.050502

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  1 in total

1.  Neural networks can learn to utilize correlated auxiliary noise.

Authors:  Aida Ahmadzadegan; Petar Simidzija; Ming Li; Achim Kempf
Journal:  Sci Rep       Date:  2021-11-03       Impact factor: 4.379

  1 in total

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