7,554 research outputs found
A sufficient Entanglement Criterion Based On Quantum Fisher Information and Variance
We derive criterion in the form of inequality based on quantum Fisher
information and quantum variance to detect multipartite entanglement. It can be
regarded as complementary of the well-established PPT criterion in the sense
that it can also detect bound entangled states. The inequality is motivated by
Y.Akbari-Kourbolagh [Phys. Rev A. 99, 012304 (2019)] which introduced
a multipartite entanglement criterion based on quantum Fisher information. Our
criterion is experimentally measurable for detecting any -qudit pure state
mixed with white noisy. We take several examples to illustrate that our
criterion has good performance for detecting certain entangled states.Comment: 11 pages, 1 figur
Concentration for unknown atomic entangled states via cavity decay
We present a physical scheme for entanglement concentration of unknown atomic
entangled states via cavity decay. In the scheme, the atomic state is used as
stationary qubit and photonic state as flying qubit, and a close maximally
entangled state can be obtained from pairs of partially entangled states
probabilistically.Comment: Three pages, Two figure
Res2Net: A New Multi-scale Backbone Architecture
Representing features at multiple scales is of great importance for numerous
vision tasks. Recent advances in backbone convolutional neural networks (CNNs)
continually demonstrate stronger multi-scale representation ability, leading to
consistent performance gains on a wide range of applications. However, most
existing methods represent the multi-scale features in a layer-wise manner. In
this paper, we propose a novel building block for CNNs, namely Res2Net, by
constructing hierarchical residual-like connections within one single residual
block. The Res2Net represents multi-scale features at a granular level and
increases the range of receptive fields for each network layer. The proposed
Res2Net block can be plugged into the state-of-the-art backbone CNN models,
e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these
models and demonstrate consistent performance gains over baseline models on
widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies
and experimental results on representative computer vision tasks, i.e., object
detection, class activation mapping, and salient object detection, further
verify the superiority of the Res2Net over the state-of-the-art baseline
methods. The source code and trained models are available on
https://mmcheng.net/res2net/.Comment: 11 pages, 7 figure
Characterization of four-qubit states via Bell inequalities
A set of Bell inequalities classifying the quantum entanglement of four-qubit
states is presented. These inequalities involve only two measurement settings
per observer and can characterize fully separable, bi-separable and
tri-separable quantum states. In addition, a quadratic inequality of the Bell
operators for four-qubit systems is derived
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