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We have decided to discontinue the publication of preprints on our preprint server as of 1 March 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.

MiS Preprint
64/2019

Information transfer in generalized probabilistic theories based on weak repeatability

Zhaoqi Wu, Shao-Ming Fei, Xianqing Li-Jost and Lin Zhang

Abstract

Information transfer in generalized probabilistic theories (GPT) is an important problem. We have dealt with the problem based on repeatability postulate, which generalizes Zurek's result to the GPT framework [Phys. Lett. A 379 (2015) 2694]. A natural question arises: can we deduce the information transfer result under weaker assumptions? In this paper, we generalize Zurek's result to the framework of GPT using weak repeatability postulate. We show that if distinguishable information can be transferred from a physical system to a series of apparatuses under the weak repeatability postulate in GPT, then the initial states of the physical system must be completely distinguishable. Moreover, after each step of invertible transformation, the composite states of the composite system composed of the physical systems and the apparatuses must also be completely distinguishable.

Received:
Jul 30, 2019
Published:
Aug 1, 2019

Related publications

inJournal
2019 Journal Open Access
Zhaoqi Wu, Shao-Ming Fei, Xianqing Li-Jost and Lin Zhang

Information transfer in generalized probabilistic theories based on weak repeatability

In: International journal of theoretical physics, 58 (2019) 11, pp. 3632-3639