Scaling of contraction costs for entanglement renormalization algorithms including tensor Trotterization and variational Monte Carlo

TL;DR

This paper analyzes the computational costs of entanglement renormalization algorithms, including tensor Trotterization and variational Monte Carlo, to optimize their efficiency for simulating quantum many-body systems.

Contribution

It provides a detailed analysis of the scaling of contraction costs and introduces optimized contraction sequences for MERA in various dimensions and strategies.

Findings

VMC offers substantial gains for 2D systems.

Optimal Trotter steps depend on bond dimension and energy accuracy.

Algorithmic phase diagrams guide the choice of MERA methods.

Abstract

The multi-scale entanglement renormalization ansatz (MERA) is a hierarchical class of tensor network states motivated by the real-space renormalization group. It is used to simulate strongly correlated quantum many-body systems. For prominent MERA structures in one and two spatial dimensions and different optimization strategies, we determine the optimal scaling of contraction costs as well as corresponding contraction sequences and algorithmic phase diagrams. This is motivated by recent efforts to employ MERA in hybrid quantum-classical algorithms, where the MERA tensors are Trotterized, i.e., chosen as circuits of quantum gates, and observables as well as energy gradients are evaluated by sampling causal-cone states. We investigate whether tensor Trotterization and/or variational Monte Carlo (VMC) sampling can lead to quantum-inspired classical MERA algorithms that perform better than the traditional optimization of full MERA based on the exact evaluation of energy gradients. Algorithmic phase diagrams indicate the best MERA method depending on the scaling of the energy accuracy and the optimal number of Trotter steps with the bond dimension. The results suggest substantial gains due to VMC for two-dimensional systems.