Andrew J. Ferris, Guifre Vidal
Monte Carlo sampling techniques have been proposed as a strategy to reduce
the computational cost of contractions in tensor network approaches to solving
many-body systems. Here we put forward a variational Monte Carlo approach for
the multi-scale entanglement renormalization ansatz (MERA), which is a unitary
tensor network. Two major adjustments are required compared to previous
proposals with non-unitary tensor networks. First, instead of sampling over
configurations of the original lattice, made of L sites, we sample over
configurations of an effective lattice, which is made of just log(L) sites.
Second, the optimization of unitary tensors must account for their unitary
character while being robust to statistical noise, which we accomplish with a
modified steepest descent method within the set of unitary tensors. We
demonstrate the performance of the variational Monte Carlo MERA approach in the
relatively simple context of a finite quantum spin chain at criticality, and
discuss future, more challenging applications, including two dimensional
systems.
View original:
http://arxiv.org/abs/1201.3975
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