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On Classification of Quantum Channels
Open Systems & Information Dynamics, 2001Quantum mutual entropy and quantum capacity are rigorously defined by Ohya, and they are quite useful in the study of quantum communication processes [4, 7, 8, 9,10]. Mathematical models of optical communication processes are described by a quantum channel and optical states, and quantum capacity is one of the most important criteria to measure the ...
Satoshi Iriyama, Noboru Watanabe
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Deep Quantum Networks for Classification
2010 20th International Conference on Pattern Recognition, 2010This paper introduces a new type of deep learning method named Deep Quantum Network (DQN) for classification. DQN inherits the capability of modeling the structure of a feature space by fuzzy sets. At first, we propose the architecture of DQN, which consists of quantum neuron and sigmoid neuron and can guide the embedding of samples divisible in new ...
Shusen Zhou +2 more
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Quantum Representation for Sentiment Classification
2022 IEEE International Conference on Quantum Computing and Engineering (QCE), 2022Fariska Z. Ruskanda +6 more
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Quantum Classification Algorithms
2020This chapter covers the topic of quantum classification algorithms. You will see how quantum classification algorithms are implemented in real life. Code examples are presented for different algorithms such as quantum classifiers, support vector machines, and sparse support vector machines.
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Classification and Construction of Quantum Communication Systems
Communications in Mathematical Physics, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Quantum Classification of Malware
2014The D-Wave architecture is a unique approach to computing which utilizes quantum annealing to solve discrete optimization problems. Applications for D-Wave machines include binary classification, complex protein-folding models, and heuristics for intractable problems such as the Traveling Salesman Problem.
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Quantum Few-Shot Image Classification
IEEE Transactions on CyberneticsFew-shot learning algorithms frequently exhibit suboptimal performance due to the limited availability of labeled data. This article presents a novel quantum few-shot image classification methodology aimed at enhancing the efficacy of few-shot learning algorithms at both the data and parameter levels.
Zhihao Huang +2 more
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Classification and decomposition of Quantum Markov Semigroups
Probability Theory and Related Fields, 2005The notions of transience and recurrence play an essential role in the study of Markov processes and in probabilistic potential theory. In [\textit{F.\,Fagnola} and \textit{R.\,Rebolledo}, Probab.\ Theory Relat.\ Fields 126, No.\,~2, 289--306 (2003; Zbl 1024.60031)], transience and recurrence were defined for quantum Markov semigroups and a dichotomy ...
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On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022Alessandro Sebastianelli +2 more
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