Literature DB >> 27768375

Quantum Change Point.

Gael Sentís1, Emilio Bagan2, John Calsamiglia2, Giulio Chiribella3,4, Ramon Muñoz-Tapia2.   

Abstract

Sudden changes are ubiquitous in nature. Identifying them is crucial for a number of applications in biology, medicine, and social sciences. Here we take the problem of detecting sudden changes to the quantum domain. We consider a source that emits quantum particles in a default state, until a point where a mutation occurs that causes the source to switch to another state. The problem is then to find out where the change occurred. We determine the maximum probability of correctly identifying the change point, allowing for collective measurements on the whole sequence of particles emitted by the source. Then, we devise online strategies where the particles are measured individually and an answer is provided as soon as a new particle is received. We show that these online strategies substantially underperform the optimal quantum measurement, indicating that quantum sudden changes, although happening locally, are better detected globally.

Year:  2016        PMID: 27768375     DOI: 10.1103/PhysRevLett.117.150502

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


  2 in total

1.  Quantum machine learning.

Authors:  Jacob Biamonte; Peter Wittek; Nicola Pancotti; Patrick Rebentrost; Nathan Wiebe; Seth Lloyd
Journal:  Nature       Date:  2017-09-13       Impact factor: 49.962

2.  Deterministic realization of collective measurements via photonic quantum walks.

Authors:  Zhibo Hou; Jun-Feng Tang; Jiangwei Shang; Huangjun Zhu; Jian Li; Yuan Yuan; Kang-Da Wu; Guo-Yong Xiang; Chuan-Feng Li; Guang-Can Guo
Journal:  Nat Commun       Date:  2018-04-12       Impact factor: 14.919

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.