Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scaling of neural-network quantum states for time evolution   Simulating quantum many-body dynamics on classical computers is a challenging
problem due to the exponential growth of the Hilbert space. Artificial neural
networks have recently been introduced as a new tool to approximate
quantum-many body states. We benchmark the variational power of the restricted
Boltzmann machine quantum states and different shallow and deep neural
autoregressive quantum states to simulate global quench dynamics of a
non-integrable quantum Ising chain. We find that the number of parameters
required to represent the quantum state at a given accuracy increases
exponentially in time. The growth rate is only slightly affected by the network
architecture over a wide range of different design choices: shallow and deep
networks, small and large filter sizes, dilated and normal convolutions, with
and without shortcut connections.

11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph