Hybrid Purification and Sampling Approach for Thermal Quantum Systems

TL;DR

This paper introduces a hybrid tensor network algorithm combining ancilla and METTS methods to efficiently study finite-temperature quantum systems, reducing variance and computational cost.

Contribution

A novel hybrid purification and sampling algorithm that improves convergence and efficiency over existing methods for thermal quantum systems.

Findings

Hybrid method converges faster at intermediate temperatures.

Reduces sample variance in measurements.

Lower computational cost due to entanglement reduction.

Abstract

We propose an algorithm which combines the beneficial aspects of two different methods for studying finite-temperature quantum systems with tensor networks. One approach is the ancilla method, which gives high-precision results but scales poorly at low temperatures. The other method is the minimally entangled typical thermal state (METTS) sampling algorithm which scales better than the ancilla method at low temperatures and can be parallelized, but requires many samples to converge to a precise result. Our proposed hybrid of these two methods purifies physical sites in a small central spatial region with partner ancilla sites, sampling the remaining sites using the METTS algorithm. Observables measured within the purified cluster have much lower sample variance than in the METTS approach, while sampling the sites outside the cluster reduces their entanglement and the computational cost of the algorithm. The sampling steps of the algorithm remain straightforwardly parallelizable. The hybrid approach also solves an important technical issue with METTS that makes it difficult to benefit from quantum number conservation. By studying S=1 Heisenberg ladder systems, we find the hybrid method converges more quickly than both the ancilla and METTS algorithms at intermediate temperatures and for systems with higher entanglement.