17,127 research outputs found
Quantum correlations in a cluster-like system
We discuss a cluster-like 1D system with triplet interaction. We study the
topological properties of this system. We find that the degeneracy depends on
the topology of the system, and well protected against external local
perturbations. All these facts show that the system is topologically ordered.
We also find a string order parameter to characterize the quantum phase
transition. Besides, we investigate two-site correlations including
entanglement, quantum discord and mutual information. We study the different
divergency behaviour of the correlations. The quantum correlation decays
exponentially in both topological and magnetic phases, and diverges in reversed
power law at the critical point. And we find that in TQPT systems, the global
difference of topology induced by dimension can be reflected in local quantum
correlations.Comment: 7 pages, 6 figure
Improved three-dimensional color-gradient lattice Boltzmann model for immiscible multiphase flows
In this paper, an improved three-dimensional color-gradient lattice Boltzmann
(LB) model is proposed for simulating immiscible multiphase flows. Compared
with the previous three-dimensional color-gradient LB models, which suffer from
the lack of Galilean invariance and considerable numerical errors in many cases
owing to the error terms in the recovered macroscopic equations, the present
model eliminates the error terms and therefore improves the numerical accuracy
and enhances the Galilean invariance. To validate the proposed model, numerical
simulation are performed. First, the test of a moving droplet in a uniform flow
field is employed to verify the Galilean invariance of the improved model.
Subsequently, numerical simulations are carried out for the layered two-phase
flow and three-dimensional Rayleigh-Taylor instability. It is shown that, using
the improved model, the numerical accuracy can be significantly improved in
comparison with the color-gradient LB model without the improvements. Finally,
the capability of the improved color-gradient LB model for simulating dynamic
multiphase flows at a relatively large density ratio is demonstrated via the
simulation of droplet impact on a solid surface.Comment: 9 Figure
Tunneling Qubit Operation on a Protected Josephson Junction Array
We discuss a protected quantum computation process based on a hexagon
Josephson junction array. Qubits are encoded in the punctured array, which is
topologically protected. The degeneracy is related to the number of holes. The
topological degeneracy is lightly shifted by tuning the flux through specific
hexagons. We also show how to perform single qubit operation and basic quantum
gate operations in this system.Comment: 8 pages, 4 figures. The published version in Phys. Rev.,
A81(2010)01232
Quantum correlations in topological quantum phase transitions
We study the quantum correlations in a 2D system that possesses a topological
quantum phase transition. The quantumness of two-body correlations is measured
by quantum discord. We calculate both the correlation of two local spins and
that of an arbitrary spin with the rest of the lattice. It is notable that
local spins are classically correlated, while the quantum correlation is hidden
in the global lattice. This is different from other systems which are not
topologically orderd. Moreover, the mutual information and global quantum
discord show critical behavior in the topological quantum phase transition.Comment: 6 pages, 3 figure
Non-canonical statistics of finite quantum system
The canonical statistics describes the statistical properties of an open
system by assuming its coupling with the heat bath infinitesimal in comparison
with the total energy in thermodynamic limit. In this paper, we generally
derive a non-canonical distribution for the open system with a finite coupling
to the heat bath, which deforms the energy shell to effectively modify the
conventional canonical way. The obtained non-canonical distribution reflects
the back action of system on the bath, and thus depicts the statistical
correlations through energy fluctuations
Quantum phase transitions in a two-dimensional quantum XYX model: Ground-state fidelity and entanglement
A systematic analysis is performed for quantum phase transitions in a
two-dimensional anisotropic spin 1/2 anti-ferromagnetic XYX model in an
external magnetic field. With the help of an innovative tensor network
algorithm, we compute the fidelity per lattice site to demonstrate that the
field-induced quantum phase transition is unambiguously characterized by a
pinch point on the fidelity surface, marking a continuous phase transition. We
also compute an entanglement estimator, defined as a ratio between the
one-tangle and the sum of squared concurrences, to identify both the
factorizing field and the critical point, resulting in a quantitative agreement
with quantum Monte Carlo simulation. In addition, the local order parameter is
"derived" from the tensor network representation of the system's ground state
wave functions.Comment: 4+ pages, 3 figure
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
For person re-identification, existing deep networks often focus on
representation learning. However, without transfer learning, the learned model
is fixed as is, which is not adaptable for handling various unseen scenarios.
In this paper, beyond representation learning, we consider how to formulate
person image matching directly in deep feature maps. We treat image matching as
finding local correspondences in feature maps, and construct query-adaptive
convolution kernels on the fly to achieve local matching. In this way, the
matching process and results are interpretable, and this explicit matching is
more generalizable than representation features to unseen scenarios, such as
unknown misalignments, pose or viewpoint changes. To facilitate end-to-end
training of this architecture, we further build a class memory module to cache
feature maps of the most recent samples of each class, so as to compute image
matching losses for metric learning. Through direct cross-dataset evaluation,
the proposed Query-Adaptive Convolution (QAConv) method gains large
improvements over popular learning methods (about 10%+ mAP), and achieves
comparable results to many transfer learning methods. Besides, a model-free
temporal cooccurrence based score weighting method called TLift is proposed,
which improves the performance to a further extent, achieving state-of-the-art
results in cross-dataset person re-identification. Code is available at
https://github.com/ShengcaiLiao/QAConv.Comment: This is the ECCV 2020 version, including the appendi
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