Quantum phase transition of sub-Ohmic spin-boson models: An approach by the multiple Davydov D2 Ansatz
Justin Tan, Nengji Zhou, Yang Zhao
TL;DR
This work advances the numerical study of sub-Ohmic spin-boson models by applying a time-independent, multi-Davydov D$_2$ variational Ansatz to three coupling scenarios: diagonal-only, dual baths with diagonal and off-diagonal couplings, and a single bath with both couplings. The approach yields critical couplings in agreement with established methods for the diagonal case, reveals first-order quantum phase transitions in the two-bath setup due to coupling competition, and introduces a rotational transformation that maps the mixed-coupling single-bath problem to a diagonal form, reducing complexity and enabling intuitive understanding. The results underscore the versatility and efficiency of the multi-D$_2$ Ansatz in capturing entanglement and bath-induced effects (via observables like $ig\
Abstract
The ground state properties and quantum phase transitions of sub-Ohmic spin-boson models are investigated using the multiple Davydov D2 Ansatz in conjunction with the variational principle. Three variants of the model are studied: (i) a single bath with diagonal coupling, (ii) two independent baths with diagonal and off-diagonal couplings, and (iii) a single bath with simultaneous diagonal and off-diagonal couplings. For the purely diagonal model, the multiple Davydov D2 Ansatz yields critical coupling strengths that are consistent with other methodologies, validating its accuracy and efficiency. In the two-bath model, the competition between diagonal and off-diagonal couplings drives a first-order transition for both symmetric and asymmetric spectral exponents, with von-Neumann entropy showing a continuous peak only under exact symmetry. Finally, for a single bath with simultaneous diagonal and off-diagonal couplings, we demonstrate that a rotational transformation maps the system to an equivalent purely diagonal model, enabling simpler and intuitive physical interpretation and reduced computational complexity.
