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Classification of Hybrid Quantum-Classical Computing
, 2023 As quantum computers mature, the applicability in practice becomes more important. Many uses of quantum computers will be hybrid, with classical computers still playing an important role in operating and using the quantum computer. The term hybrid is however diffuse and multi-interpretable. In this work we define two classes of hybrid quantum-classical
Frank Phillipson +2 more
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Quantum cluster algorithm for data classification [PDF]
Materials Theory, 2021 AbstractWe present a quantum algorithm for data classification based on the nearest-neighbor learning algorithm. The classification algorithm is divided into two steps: Firstly, data in the same class is divided into smaller groups with sublabels assisting building boundaries between data with different labels.
Junxu Li, Sabre Kais
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Classification of Quantum Tori with Involution [PDF]
Canadian Mathematical Bulletin, 2002 AbstractQuantum tori with graded involution appear as coordinate algebras of extended affine Lie algebras of type A1, C and BC. We classify them in the category of algebras with involution. From this, we obtain precise information on the root systems of extended affine Lie algebras of type C.
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Quantum Machine Learning with SQUID [PDF]
Quantum, 2022 In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems.
Alessandro Roggero +3 more
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Unified framework for quantum classification
Physical Review Research, 2021 Quantum machine learning is an emerging field that combines machine learning with advances in quantum technologies. Many works have suggested great possibilities of using near-term quantum hardware in supervised learning. Motivated by these developments,
Nhat A. Nghiem +2 more
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Generalization in Quantum Machine Learning: A Quantum Information Standpoint
PRX Quantum, 2021 Quantum classification and hypothesis testing (state and channel discrimination) are two tightly related subjects, the main difference being that the former is data driven: how to assign to quantum states ρ(x) the corresponding class c (or hypothesis) is
Leonardo Banchi +2 more
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A Comparative Study of Quantum Feature Maps and Quantum Classifiers for Heart Disease Prediction
AIThis research introduces a quantum machine learning (QML) approach for predicting heart disease (HD). The method combines preprocessing of data with quantum feature map (QFM) and quantum classification techniques.
Muhammad Minoar Hossain +2 more
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A dual quantum image feature extraction method: PSQIFE
IET Image Processing, 2022 In digital image processing, feature extraction occupies a very important position, which is related to the effect of image classification or recognition. At present, effective quantum feature extraction methods are relatively lacking.
Jie Su, Shuhan Lu, Lin Li
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Implementing Magnetic Resonance Imaging Brain Disorder Classification via AlexNet–Quantum Learning
Mathematics, 2023 The classical neural network has provided remarkable results to diagnose neurological disorders against neuroimaging data. However, in terms of efficient and accurate classification, some standpoints need to be improved by utilizing high-speed computing ...
Naif Alsharabi +3 more
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Quantum discriminator for binary classification
Scientific ReportsAbstractQuantum computers have the unique ability to operate relatively quickly in high-dimensional spaces—this is sought to give them a competitive advantage over classical computers. In this work, we propose a novel quantum machine learning model called the Quantum Discriminator, which leverages the ability of quantum computers to operate in the high-
Prasanna Date, Wyatt Smith
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