Literature DB >> 22453835

Quantum mechanics can reduce the complexity of classical models.

Mile Gu1, Karoline Wiesner, Elisabeth Rieper, Vlatko Vedral.   

Abstract

Mathematical models are an essential component of quantitative science. They generate predictions about the future, based on information available in the present. In the spirit of simpler is better; should two models make identical predictions, the one that requires less input is preferred. Yet, for almost all stochastic processes, even the provably optimal classical models waste information. The amount of input information they demand exceeds the amount of predictive information they output. Here we show how to systematically construct quantum models that break this classical bound, and that the system of minimal entropy that simulates such processes must necessarily feature quantum dynamics. This indicates that many observed phenomena could be significantly simpler than classically possible should quantum effects be involved.

Year:  2012        PMID: 22453835     DOI: 10.1038/ncomms1761

Source DB:  PubMed          Journal:  Nat Commun        ISSN: 2041-1723            Impact factor:   14.919


  6 in total

1.  Regularities unseen, randomness observed: levels of entropy convergence.

Authors:  James P Crutchfield; David P Feldman
Journal:  Chaos       Date:  2003-03       Impact factor: 3.642

2.  Quantifying self-organization with optimal predictors.

Authors:  Cosma Rohilla Shalizi; Kristina Lisa Shalizi; Robert Haslinger
Journal:  Phys Rev Lett       Date:  2004-09-10       Impact factor: 9.161

3.  Inferring statistical complexity.

Authors: 
Journal:  Phys Rev Lett       Date:  1989-07-10       Impact factor: 9.161

4.  Multiscale complex network of protein conformational fluctuations in single-molecule time series.

Authors:  Chun-Biu Li; Haw Yang; Tamiki Komatsuzaki
Journal:  Proc Natl Acad Sci U S A       Date:  2008-01-04       Impact factor: 11.205

5.  Time's barbed arrow: irreversibility, crypticity, and stored information.

Authors:  James P Crutchfield; Christopher J Ellison; John R Mahoney
Journal:  Phys Rev Lett       Date:  2009-08-28       Impact factor: 9.161

6.  A measure for brain complexity: relating functional segregation and integration in the nervous system.

Authors:  G Tononi; O Sporns; G M Edelman
Journal:  Proc Natl Acad Sci U S A       Date:  1994-05-24       Impact factor: 11.205

  6 in total
  5 in total

1.  Occam's Quantum Strop: Synchronizing and Compressing Classical Cryptic Processes via a Quantum Channel.

Authors:  John R Mahoney; Cina Aghamohammadi; James P Crutchfield
Journal:  Sci Rep       Date:  2016-02-15       Impact factor: 4.379

2.  Experimentally modeling stochastic processes with less memory by the use of a quantum processor.

Authors:  Matthew S Palsson; Mile Gu; Joseph Ho; Howard M Wiseman; Geoff J Pryde
Journal:  Sci Adv       Date:  2017-02-03       Impact factor: 14.136

3.  Extreme Quantum Advantage when Simulating Classical Systems with Long-Range Interaction.

Authors:  Cina Aghamohammadi; John R Mahoney; James P Crutchfield
Journal:  Sci Rep       Date:  2017-07-27       Impact factor: 4.379

4.  Interfering trajectories in experimental quantum-enhanced stochastic simulation.

Authors:  Farzad Ghafari; Nora Tischler; Carlo Di Franco; Jayne Thompson; Mile Gu; Geoff J Pryde
Journal:  Nat Commun       Date:  2019-04-09       Impact factor: 14.919

5.  Quantum Statistical Complexity Measure as a Signaling of Correlation Transitions.

Authors:  André T Cesário; Diego L B Ferreira; Tiago Debarba; Fernando Iemini; Thiago O Maciel; Reinaldo O Vianna
Journal:  Entropy (Basel)       Date:  2022-08-19       Impact factor: 2.738

  5 in total

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