Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantum Bayesian decision-making*   As a compact representation of joint probability distributions over a
dependence graph of random variables, and a tool for modelling and reasoning in
the presence of uncertainty, Bayesian networks are of great importance for
artificial intelligence to combine domain knowledge, capture causal
relationships, or learn from incomplete datasets. Known as a NP-hard problem in
a classical setting, Bayesian inference pops up as a class of algorithms worth
to explore in a quantum framework. This paper explores such a research
direction and improves on previous proposals by a judicious use of the utility
function in an entangled configuration. It proposes a completely quantum
mechanical decision-making process with a proven computational advantage. A
prototype implementation in Qiskit (a Python-based program development kit for
the IBM Q machine) is discussed as a proof-of-concept.

11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat