scholarly journals Topological hardcore bosons on the honeycomb lattice

2016 ◽  
Vol 94 (9) ◽  
pp. 814-820
Author(s):  
S.A. Owerre

This paper presents a connection between the topological properties of hardcore bosons and that of magnons in quantum spin magnets. We utilize the Haldane-like hardcore bosons on the honeycomb lattice as an example. We show that this system maps to a spin-1/2 quantum XY model with a next-nearest-neighbour Dzyaloshinsky–Moriya interaction. We obtain the magnon excitations of the quantum spin model and compute the edge states, Berry curvature, and thermal and spin Nernst conductivities. Because of the mapping from spin variables to bosons, the hardcore bosons possess the same nontrivial topological properties as those in quantum spin systems. These results are important in the study of magnetic excitations in quantum magnets and they are also useful for understanding the control of ultracold bosonic quantum gases in honeycomb optical lattices, which is experimentally accessible.

1978 ◽  
Vol 76 (2) ◽  
pp. 213-219 ◽  
Author(s):  
Richard C. Brower ◽  
Miguel A. Furman ◽  
Moshe Moshe

Author(s):  
Eric Zou ◽  
Erik Long ◽  
Erhai Zhao

Abstract Neural network quantum states provide a novel representation of the many-body states of interacting quantum systems and open up a promising route to solve frustrated quantum spin models that evade other numerical approaches. Yet its capacity to describe complex magnetic orders with large unit cells has not been demonstrated, and its performance in a rugged energy landscape has been questioned. Here we apply restricted Boltzmann machines and stochastic gradient descent to seek the ground states of a compass spin model on the honeycomb lattice, which unifies the Kitaev model, Ising model and the quantum 120-degree model with a single tuning parameter. We report calculation results on the variational energy, order parameters and correlation functions. The phase diagram obtained is in good agreement with the predictions of tensor network ansatz, demonstrating the capacity of restricted Boltzmann machines in learning the ground states of frustrated quantum spin Hamiltonians. The limitations of the calculation are discussed. A few strategies are outlined to address some of the challenges in machine learning frustrated quantum magnets.


1995 ◽  
Vol 52 (2) ◽  
pp. 1436-1446 ◽  
Author(s):  
Varsha Banerjee ◽  
Sushanta Dattagupta ◽  
Parongama Sen

1995 ◽  
Vol 140-144 ◽  
pp. 1497-1498 ◽  
Author(s):  
J. Richter ◽  
A. Voigt ◽  
S. Krüger

2007 ◽  
Vol 75 (18) ◽  
Author(s):  
A. Collins ◽  
J. McEvoy ◽  
D. Robinson ◽  
C. J. Hamer ◽  
Zheng Weihong

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