Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Symmetry Enforced Self-Learning Monte Carlo Method Applied to the
  Holstein Model   Self-learning Monte Carlo method (SLMC), using a trained effective model to
guide Monte Carlo sampling processes, is a powerful general-purpose numerical
method recently introduced to speed up simulations in (quantum) many-body
systems. In this work, we further improve the efficiency of SLMC by enforcing
physical symmetries on the effective model. We demonstrate its effectiveness in
the Holstein Hamiltonian, one of the most fundamental many-body descriptions of
electron-phonon coupling. Simulations of the Holstein model are notoriously
difficult due to the combination of the typical cubic scaling of fermionic
Monte Carlo and the presence of extremely long autocorrelation times. Our
method addresses both bottlenecks. This enables simulations on large lattices
in the most difficult parameter regions, and evaluation of the critical point
for the charge density wave transition at half-filling with high precision. We
argue that our work opens a new research area of quantum Monte Carlo (QMC),
providing a general procedure to deal with ergodicity in situations involving
Hamiltonians with multiple, distinct low energy states.

20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin