629 research outputs found
Itinerant quantum critical point with frustration and non-Fermi-liquid
Employing the self-learning quantum Monte Carlo algorithm, we investigate the
frustrated transverse-field triangle-lattice Ising model coupled to a Fermi
surface. Without fermions, the spin degrees of freedom undergoes a second-order
quantum phase transition between paramagnetic and clock-ordered phases. This
quantum critical point (QCP) has an emergent U(1) symmetry and thus belongs to
the (2+1)D XY universality class. In the presence of fermions, spin
fluctuations introduce effective interactions among fermions and distort the
bare Fermi surface towards an interacting one with hot spots and Fermi pockets.
Near the QCP, non-Fermi-liquid behavior are observed at the hot spots, and the
QCP is rendered into a different universality with Hertz-Millis type exponents.
The detailed properties of this QCP and possibly related experimental systems
are also discussed.Comment: 9 pages, 8 figure
Self-Learning Monte Carlo Method
Monte Carlo simulation is an unbiased numerical tool for studying classical
and quantum many-body systems. One of its bottlenecks is the lack of general
and efficient update algorithm for large size systems close to phase transition
or with strong frustrations, for which local updates perform badly. In this
work, we propose a new general-purpose Monte Carlo method, dubbed self-learning
Monte Carlo (SLMC), in which an efficient update algorithm is first learned
from the training data generated in trial simulations and then used to speed up
the actual simulation. We demonstrate the efficiency of SLMC in a spin model at
the phase transition point, achieving a 10-20 times speedup.Comment: add more refs and correct some typo
Competing pairing channels in the doped honeycomb lattice Hubbard model
Proposals for superconductivity emerging from correlated electrons in the
doped Hubbard model on the honeycomb lattice range from chiral singlet
to triplet pairing, depending on the considered range of doping and
interaction strength, as well as the approach used to analyze the pairing
instabilities. Here, we consider these scenarios using large-scale dynamic
cluster approximation (DCA) calculations to examine the evolution in the
leading pairing symmetry from weak to intermediate coupling strength. These
calculations focus on doping levels around the van Hove singularity (VHS) and
are performed using DCA simulations with an interaction-expansion
continuous-time quantum Monte Carlo cluster solver. We calculated explicitly
the temperature dependence of different uniform superconducting pairing
susceptibilities and found a consistent picture emerging upon gradually
increasing the cluster size: while at weak coupling the singlet pairing
dominates close to the VHS filling, an enhanced tendency towards -wave
triplet pairing upon further increasing the interaction strength is observed.
The relevance of these systematic results for existing proposals and ongoing
pursuits of odd-parity topological superconductivity are also discussed.Comment: 7 pages, 5 figure
Self-Learning Monte Carlo Method in Fermion Systems
We develop the self-learning Monte Carlo (SLMC) method, a general-purpose
numerical method recently introduced to simulate many-body systems, for
studying interacting fermion systems. Our method uses a highly-efficient update
algorithm, which we design and dub "cumulative update", to generate new
candidate configurations in the Markov chain based on a self-learned bosonic
effective model. From general analysis and numerical study of the double
exchange model as an example, we find the SLMC with cumulative update
drastically reduces the computational cost of the simulation, while remaining
statistically exact. Remarkably, its computational complexity is far less than
the conventional algorithm with local updates
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