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Quantum Sensing and Communication via Non-Gaussian States

Submitted by admin on Mon, 11/11/2024 - 19:45
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).

Source Coding for Markov Sources With Partial Memoryless Side Information at the Decoder

Submitted by admin on Mon, 11/11/2024 - 19:45
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.

Deviation From Maximal Entanglement for Mid-Spectrum Eigenstates of Local Hamiltonians

Submitted by admin on Thu, 10/31/2024 - 20:45
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.

Fisher Information Under Local Differential Privacy

Submitted by admin on Mon, 10/28/2024 - 01:24

We develop data processing inequalities that describe how Fisher information from statistical samples can scale with the privacy parameter $\varepsilon $ under local differential privacy constraints. These bounds are valid under general conditions on the distribution of the score of the statistical model, and they elucidate under which conditions the dependence on $\varepsilon $ is linear, quadratic, or exponential.