We introduce a Metropolis-Hastings Markov chain for Boltzmann distributions of classical spin systems. It relies on approximate tensor network contractions to propose correlated collective updates at each step of the evolution. We present benchmark computations for a wide variety of instances of the two-dimensional Ising model, including ferromagnetic, antiferromagnetic, (fully) frustrated and Edwards-Anderson spin glass instances, and we show that, with modest computational effort, our Markov chain achieves sizeable acceptance rates, even in the vicinity of critical points. In each of the situations we have considered, the Markov chain compares well with other Monte Carlo schemes such as the Metropolis or Wolff's algorithm: equilibration times appear to be reduced by a factor that varies between $40$ and $2000$, depending on the model and the observable being monitored. We also present an extension to three spatial dimensions, and demonstrate that it exhibits fast equilibration for finite ferro- and antiferromagnetic instances. Additionally, and although it is originally designed for a square lattice of finite degrees of freedom with open boundary conditions, the proposed scheme can be used as such, or with slight modifications, to study triangular lattices, systems with continuous degrees of freedom, matrix models, a confined gas of hard spheres, or to deal with arbitrary boundary conditions.
Authors / Affiliations: mappings to Contributors and OrganizationsSee all Organizations.
- 1 2 3 Miguel Frías Pérez,
- 4 Michael Mariën,
- 5 6 David Pérez García,
- 1 2 Mari Carmen Bañuls,
- 3 5 Sofyan Iblisdir
- 1 Max-Planck-Institut für Quantenoptik / Max Planck Institute of Quantum Optics [MPQ]
- 2 Munich Center for Quantum Science and Technology [MCQST]
- 3 Institut de Ciènces del Cosmos / Institute of Cosmos Sciences, University of Barcelona [ICCUB]
- 4 KBC Groep / KBC Group
- 5 Universidad Complutense de Madrid / Complutense University of Madrid
- 6 Instituto de Ciencias Matemáticas / Institute of Mathematical Sciences [ICMAT]