4,265 research outputs found
Noise robustness of the nonlocality of entangled quantum states
We study the nonlocal properties of states resulting from the mixture of an
arbitrary entangled state rho of two d-dimensional systems and completely
depolarized noise, with respective weights p and 1-p. We first construct a
local model for the case in which rho is maximally entangled and p at or below
a certain bound. We then extend the model to arbitrary rho. Our results provide
bounds on the resistance to noise of the nonlocal correlations of entangled
states. For projective measurements, the critical value of the noise parameter
p for which the state becomes local is at least asymptotically log(d) larger
than the critical value for separability.Comment: 5 pages, no figures. It contains only minor changes, published
versio
Noise robustness in the detection of non separable random unitary maps
We briefly review a recently proposed method to detect properties of quantum
noise processes and quantum channels. We illustrate in detail the method for
detecting non separable random unitary channels and consider in particular the
explicit examples of the CNOT and CZ gates. We analyse their robustness in the
presence of noise for several quantum noise models.Comment: 10 pages, 1 figur
Wavelet Integrated CNNs for Noise-Robust Image Classification
Convolutional Neural Networks (CNNs) are generally prone to noise
interruptions, i.e., small image noise can cause drastic changes in the output.
To suppress the noise effect to the final predication, we enhance CNNs by
replacing max-pooling, strided-convolution, and average-pooling with Discrete
Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers
applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and
design wavelet integrated CNNs (WaveCNets) using these layers for image
classification. In WaveCNets, feature maps are decomposed into the
low-frequency and high-frequency components during the down-sampling. The
low-frequency component stores main information including the basic object
structures, which is transmitted into the subsequent layers to extract robust
high-level features. The high-frequency components, containing most of the data
noise, are dropped during inference to improve the noise-robustness of the
WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy
version of ImageNet) show that WaveCNets, the wavelet integrated versions of
VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness
than their vanilla versions.Comment: CVPR accepted pape
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