Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine learning transfer efficiencies for noisy quantum walks   Quantum effects are known to provide an advantage in particle transfer across
networks. In order to achieve this advantage, requirements on both a graph type
and a quantum system coherence must be found. Here we show that the process of
finding these requirements can be automated by learning from simulated
examples. The automation is done by using a convolutional neural network of a
particular type that learns to understand with which network and under which
coherence requirements quantum advantage is possible. Our machine learning
approach is applied to study noisy quantum walks on cycle graphs of different
sizes. We found that it is possible to predict the existence of quantum
advantage for the entire decoherence parameter range, even for graphs outside
of the training set. Our results are of importance for demonstration of
advantage in quantum experiments and pave the way towards automating scientific
research and discoveries.

26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
3
The categories of this paper are: physics