Bounding and Estimating the Classical Information Rate of Quantum Channels with Memory

We consider the scenario of classical communication over a finite-dimensional quantum channel with memory using a separable-state input ensemble and local output measurements. We propose algorithms for estimating the information rate of such communication setups, along with algorithms for bounding the information rate based on so-called auxiliary channels. Some of the algorithms are extensions of their counterparts for (classical) finite-state-machine channels. Notably, we discuss suitable graphical models for doing the relevant computations. Moreover, the auxiliary channels are learned in a data-driven approach; i.e., only input/output sequences of the true channel are needed, but not the channel model of the true channel.Comment: This work has been submitted to the IEEE Transactions on Information Theory for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Estimating the Information Rate of a Channel with Classical Input and Output and a Quantum State (Extended Version)

We consider the problem of transmitting classical information over a time-invariant channel with memory. A popular class of time-invariant channels with memory are finite-state-machine channels, where a \emph{classical} state evolves over time and governs the relationship between the classical input and the classical output of the channel. For such channels, various techniques have been developed for estimating and bounding the information rate. In this paper we consider a class of time-invariant channels where a \emph{quantum} state evolves over time and governs the relationship between the classical input and the classical output of the channel. We propose algorithms for estimating and bounding the information rate of such channels. In particular, we discuss suitable graphical models for doing the relevant computations.Comment: This is an extended version of a paper that appears in Proc. 2017 IEEE International Symposium on Information Theory, Aachen, Germany, June 201