TL;DR
This paper introduces an iterative optimization method for the MERA tensor network ansatz, enabling efficient analysis of low-energy states in quantum many-body systems, with applications to various 1D models.
Contribution
It presents a novel iterative optimization algorithm for MERA, including specialized methods for infinite systems, improving computational efficiency for quantum lattice models.
Findings
Efficient optimization of MERA for 1D systems demonstrated.
Accurate computation of local observables and correlators achieved.
Benchmark results on Ising, Potts, XX, and Heisenberg models provided.
Abstract
We describe an iterative method to optimize the multi-scale entanglement renormalization ansatz (MERA) for the low-energy subspace of local Hamiltonians on a D-dimensional lattice. For translation invariant systems the cost of this optimization is logarithmic in the linear system size. Specialized algorithms for the treatment of infinite systems are also described. Benchmark simulation results are presented for a variety of 1D systems, namely Ising, Potts, XX and Heisenberg models. The potential to compute expected values of local observables, energy gaps and correlators is investigated.
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