Locality optimization for parent Hamiltonians of Tensor Networks
Giuliano Giudici, J. Ignacio Cirac, Norbert Schuch

TL;DR
This paper introduces an efficient semidefinite programming algorithm to simplify parent Hamiltonians of tensor network states, reducing complex multi-spin interactions to simpler, more local terms while preserving the ground state.
Contribution
It develops a systematic method to optimize and simplify parent Hamiltonians, making them more local and easier to analyze or implement.
Findings
Successfully reduces AKLT and Toric Code Hamiltonians to 2- and 4-body interactions.
Produces a simplified 4-body Hamiltonian for the RVB state on the kagome lattice.
Demonstrates significant reduction in interaction complexity while maintaining ground state fidelity.
Abstract
Tensor Network states form a powerful framework for both the analytical and numerical study of strongly correlated phases. Vital to their analytical utility is that they appear as the exact ground states of associated parent Hamiltonians, where canonical proof techniques guarantee a controlled ground space structure. Yet, while those Hamiltonians are local by construction, the known techniques often yield complex Hamiltonians which act on a rather large number of spins. In this paper, we present an algorithm to systematically simplify parent Hamiltonians, breaking them down into any given basis of elementary interaction terms. The underlying optimization problem is a semidefinite program, and thus the optimal solution can be found efficiently. Our method exploits a degree of freedom in the construction of parent Hamiltonians -- the excitation spectrum of the local terms -- over which it…
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