X-Symbols for Non-Abelian Symmetries in Tensor Networks

TL;DR

This paper introduces X-symbols, a new mathematical tool that simplifies and accelerates the implementation of non-abelian symmetries in tensor network calculations, enhancing both theoretical understanding and computational efficiency.

Contribution

It presents a novel method using X-symbols to handle non-abelian symmetries in tensor networks, enabling deterministic computation and tabulation for improved efficiency.

Findings

X-symbols can be computed once and used universally.

X-symbols are smaller than their associated Clebsch-Gordan tensors.

Using X-symbols significantly speeds up tensor network calculations.

Abstract

The full exploitation of non-abelian symmetries in tensor network states (TNS) derived from a given lattice Hamiltonian is highly attractive in various aspects. From a theoretical perspective, it can offer deep insights into the entanglement structure and quantum information content of strongly correlated quantum many-body states. From a practical perspective, it allows one to push numerical efficiency by orders of magnitude. Physical expectation values based on TNS require the full contraction of a given tensor network, with the elementary ingredient being a pairwise contraction. While well-established for no or just abelian symmetries, this can become quickly extremely involved and cumbersome for general non-abelian symmetries. As shown in this work, however, the latter can be tackled in a transparent and efficient manner by introducing so-called X-symbols which deal with the underlying pairwise contraction of generalized Clebsch-Gordan tensors (CGTs). These X-symbols can be computed deterministically once and for all, and hence also be tabulated. Akin to 6j-symbols, X-symbols are generally much smaller than their constituting CGTs. In applications, they solely affect the tensors of reduced matrix elements, and therefore, once tabulated, allow one to completely sidestep the explicit usage of CGTs, and thus to greatly increase numerical efficiency.