Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimating the degree of non-Markovianity using machine learning   In the last years, the application of machine learning methods has become
increasingly relevant in different fields of physics. One of the most
significant subjects in the theory of open quantum systems is the study of the
characterization of non-Markovian memory effects that emerge dynamically
throughout the time evolution of open systems as they interact with their
surrounding environment. Here we consider two well-established quantifiers of
the degree of memory effects, namely, the trace distance and the
entanglement-based measures of non-Markovianity. We demonstrate that using
machine learning techniques, in particular, support vector machine algorithms,
it is possible to estimate the degree of non-Markovianity in two paradigmatic
open system models with high precision. Our approach can be experimentally
feasible to estimate the degree of non-Markovianity, since it requires a single
or at most two rounds of state tomography.

25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg