Optimizing Quantum Classification Algorithms on Classical Benchmark Datasets. [PDF]
Entropy (Basel), 2023 The discovery of quantum algorithms offering provable advantages over the best known classical alternatives, together with the parallel ongoing revolution brought about by classical artificial intelligence, motivates a search for applications of quantum ...
John M +4 more
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Case-Based and Quantum Classification for ERP-Based Brain–Computer Interfaces [PDF]
Brain Sciences, 2023 Low transfer rates are a major bottleneck for brain–computer interfaces based on electroencephalography (EEG). This problem has led to the development of more robust and accurate classifiers.
Grégoire H. Cattan, Alexandre Quemy
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Unified framework for quantum classification [PDF]
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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On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms
Big Data Mining and Analytics, 2020 This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms.
Farid Ablayev +5 more
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Quantum machine learning for image classification [PDF]
Machine Learning: Science and Technology, 2023 Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the ...
Arsenii Senokosov +4 more
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Optimal provable robustness of quantum classification via quantum hypothesis testing [PDF]
npj Quantum Information, 2021 AbstractQuantum machine learning models have the potential to offer speedups and better predictive accuracy compared to their classical counterparts. However, these quantum algorithms, like their classical counterparts, have been shown to also be vulnerable to input perturbations, in particular for classification problems.
Maurice Weber +4 more
openaire +6 more sources
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M-type classification [PDF]
Scientific Reports, 2023 The functional characterization of different neuronal types has been a longstanding and crucial challenge. With the advent of physical quantum computers, it has become possible to apply quantum machine learning algorithms to translate theoretical ...
Xavier Vasques, Hanhee Paik, Laura Cif
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Deep learning for classifying quantum emission signals in WS2 monolayers using wavelet transform [PDF]
Scientific ReportsThis study aimed to develop and evaluate deep learning approaches for the classification of quantum emission signals from WS2 monolayer nanobubbles across multiple spectral bands, addressing challenges in quantum materials characterization and spectral ...
Hossein Najafzadeh +4 more
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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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Hybrid Quantum–Classical Neural Networks for Efficient MNIST Binary Image Classification
MathematicsImage classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks.
Deepak Ranga +4 more
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