This paper studies the rate-distortion-perception (RDP) tradeoff for a Gaussian vector source coding problem where the goal is to compress the multi-component source subject to distortion and perception constraints. Specifically, the RDP setting with either the Kullback-Leibler (KL) divergence or Wasserstein-2 metric as the perception loss function is examined, and it is shown that for Gaussian vector sources, jointly Gaussian reconstructions are optimal.

Quantum sensing and communication (QSC) is pivotal for developing next-generation networks with unprecedented performance. Many implementations of existing QSC systems employ Gaussian states as they can be easily realized using current technologies. However, Gaussian states lack non-classical properties necessary to unleash the full potential of QSC. This motivates the use of non-Gaussian states, which have non-classical properties beneficial for QSC. This paper establishes a theoretical foundation for QSC employing photon-varied Gaussian states (PVGSs).

We consider the one helper source coding problem posed and investigated by Ahlswede, Körner, and Wyner for a class of information sources with memory. For this class of information sources we give explicit inner and outer bounds of the admissible rate region. We also give a certain nontrivial class of information sources where the inner and outer bounds match.

In a spin chain governed by a local Hamiltonian, we consider a microcanonical ensemble in the middle of the energy spectrum and a contiguous subsystem whose length is a constant fraction of the system size. We prove that if the bandwidth of the ensemble is greater than a certain constant, then the average entanglement entropy (between the subsystem and the rest of the system) of eigenstates in the ensemble deviates from the maximum entropy by at least a positive constant.