Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Liubov A. Markovich"'
Autor:
Daniel Stilck França, Liubov A. Markovich, V. V. Dobrovitski, Albert H. Werner, Johannes Borregaard
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-11 (2024)
Abstract Characterizing the interactions and dynamics of quantum mechanical systems is an essential task in developing quantum technologies. We propose an efficient protocol based on the estimation of the time-derivatives of few qubit observables usi
Externí odkaz:
https://doaj.org/article/17e6b21f689d4228b0e34c72eee710e9
Publikováno v:
Entropy, Vol 26, Iss 3, p 176 (2024)
This paper delves into the significance of the tomographic probability density function (pdf) representation of quantum states, shedding light on the special classes of pdfs that can be tomograms. Instead of using wave functions or density operators
Externí odkaz:
https://doaj.org/article/3124db60bf6448b8b01e6b0760f4428d
Publikováno v:
Entropy, Vol 25, Iss 2, p 309 (2023)
We introduce the concept of the almost-companion matrix (ACM) by relaxing the non-derogatory property of the standard companion matrix (CM). That is, we define an ACM as a matrix whose characteristic polynomial coincides with a given monic and genera
Externí odkaz:
https://doaj.org/article/6db24933ac104f59ba43a9a82a4a7d92
Publikováno v:
Entropy, Vol 21, Iss 9, p 870 (2019)
This paper proposes an alternative geometric representation of single qudit states based on probability simplexes to describe the quantum properties of noncomposite systems. In contrast to the known high dimension pictures, we present the planar pict
Externí odkaz:
https://doaj.org/article/ac222296fa8245e4b838b80f78338126
Autor:
Liubov A. Markovich
Publikováno v:
Lithuanian Mathematical Journal. 55:413-432
The processing of stationary random sequences under nonparametric uncertainty is given by a filtering problem when the signal distribution is unknown. A useful signal (Sn)n≽1 is assumed to be Markovian. This assumption allows us to estimate the unk
Autor:
A. V. Dobrovidov, Liubov A. Markovich
Publikováno v:
MIM
We consider nonparametric estimation of the derivative of a probability density function with the bounded support [0, ∞). Estimators are looked up in the class estimators with asymmetric gamma kernel functions. The use of gamma kernels is due to th
Autor:
Liubov A. Markovich, A. V. Dobrovidov
Publikováno v:
ALCOSP
In some applications it is necessary to estimate derivatives of probability densities defined on the positive semi-axis. The quality of nonparametric estimates of the probability densities and their derivatives are strongly influenced by smoothing pa