Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning optimal quantum models is NP-hard   Physical modeling closes the gap between perception in terms of measurements
and abstraction in terms of theoretical models. Physical modeling is a major
objective in physics and is generally regarded as a creative process. How good
are computers at solving this task? This question is both of philosophical and
practical interest because a positive answer would allow an artificial
intelligence to understand the physical world. Quantum mechanics is the most
fundamental physical theory and there is a deep belief that nature follows the
rules of quantum mechanics. Hence, we raise the question whether computers are
able to learn optimal quantum models from measured data. Here we show that in
the absence of physical heuristics, the inference of optimal quantum models
cannot be computed efficiently (unless P = NP). This result illuminates
rigorous limits to the extent to which computers can be used to further our
understanding of nature.

9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an