Additive Invariants on Quantum Channels and Regularized Minimum Entropy

AbstractGiven two quantum channels, we examine the task of determining whether they are compatible—meaning that one can perform both channels simultaneously but, in the future, choose exactly one channel whose output is desired (while forfeiting the output of the other channel). Here, we present several results concerning this task. First, we show it is equivalent to the quantum state marginal problem, i.e., every quantum state marginal problem can be recast as the compatibility of two channels, and vice versa. Second, we show that compatible measure-and-prepare channels (i.e., entanglement-breaking channels) do not necessarily have a measure-and-prepare compatibilizing channel. Third, we extend the notion of the Jordan product of matrices to quantum channels and present sufficient conditions for channel compatibility. These Jordan products and their generalizations might be of independent interest. Last, we formulate the different notions of compatibility as semidefinite programs and numerically test when families of partially dephasing-depolarizing channels are compatible.

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Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

Applied Sciences ◽

10.3390/app11146590 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6590

Author(s):

Krittakom Srijiranon ◽

Narissara Eiamkanitchat

Keyword(s):

Particulate Matter ◽

Fuzzy Model ◽

Original Data ◽

Northern Region ◽

National Standard ◽

Minimum Entropy ◽

Fuzzy Transformation ◽

Neuro Fuzzy ◽

Proposed Model ◽

Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

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An enhanced binarization framework for degraded historical document images

EURASIP Journal on Image and Video Processing ◽

10.1186/s13640-021-00556-4 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Wei Xiong ◽

Lei Zhou ◽

Ling Yue ◽

Lirong Li ◽

Song Wang

Keyword(s):

Document Image ◽

Morphological Operations ◽

Document Images ◽

Minimum Entropy ◽

Stroke Width ◽

Background Estimation ◽

Structuring Element ◽

Document Image Binarization ◽

Benchmark Datasets ◽

Stroke Width Transform

AbstractBinarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

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Maximum relative entropy of coherence for quantum channels

Science China Physics Mechanics and Astronomy ◽

10.1007/s11433-021-1709-9 ◽

2021 ◽

Vol 64 (8) ◽

Author(s):

Zhi-Xiang Jin ◽

Long-Mei Yang ◽

Shao-Ming Fei ◽

Xianqing Li-Jost ◽

Zhi-Xi Wang ◽

...

Keyword(s):

Relative Entropy ◽

Quantum Channels ◽

Relative Entropy Of Coherence

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