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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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Quaternion Quantum Neural Network for Classification
Advances in Applied Clifford Algebras, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guillermo Altamirano-Escobedo +1 more
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Quantum Classification Outside the Promised Class
De ComputisThis paper studies the important problem of quantum classification of Boolean functions from an entirely novel perspective. Typically, quantum classification algorithms allow us to classify functions with a probability of 1.0, if we are promised that ...
T. Andronikos +4 more
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Quantum classification for synthetic aperture radar
Defense + Commercial SensingThe field of quantum computing, especially quantum machine learning (QML), has been the subject of much research in recent years. Leveraging the quantum properties of superposition and entanglement promises exponential decrease in computation costs. With
Salil Naik +4 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
In artificial intelligence systems, a question on how to express the uncertainty in knowledge remains an open issue. The negation scheme provides a new perspective to solve this issue.
Fuyuan Xiao, W. Pedrycz
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In artificial intelligence systems, a question on how to express the uncertainty in knowledge remains an open issue. The negation scheme provides a new perspective to solve this issue.
Fuyuan Xiao, W. Pedrycz
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Quantum-Assisted Hierarchical Fuzzy Neural Network for Image Classification
IEEE transactions on fuzzy systemsDeep learning is a powerful technique for data-driven learning in the era of Big Data. However, most deep learning models are deterministic models that ignore the uncertainty of data. Fuzzy neural networks are proposed to tackle this type of problem.
Shengyao Wu +5 more
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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, Jinjing Shi, Xuelong Li
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Quantum K‐Nearest Neighbor Classification Algorithm via a Divide‐and‐Conquer Strategy
Advanced Quantum TechnologiesThe K‐nearest neighbor algorithm is one of the most frequently applied supervised machine learning algorithms. Similarity computing is considered to be the most crucial and time‐consuming step among the classical K‐nearest neighbor algorithm. A quantum K‐
Li‐Hua Gong +4 more
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Classical-quantum approach to image classification: Autoencoders and quantum SVMs
AVS Quantum ScienceIn order to leverage quantum computers for machine learning tasks such as image classification, consideration is required. Noisy Intermediate-Scale Quantum (NISQ) computers have limitations that include noise, scalability, read-in and read-out times, and
Donovan Slabbert, Francesco Petruccione
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