Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection

Phillipson, Frank; Wezeman, Robert; Chiscop, Irina

doi:10.1007/978-3-030-70866-5_11

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Also, the performance proposed approach is compared with different quantum kernels and classical kernel RBF. Due to the limited quantum hardware resources, the advantage of quantum SVM is not clear enough in real applications [19,29]. In future work, more quantum kernels can be applied with real quantum devices.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the limited quantum hardware resources, the advantage of quantum SVM is not clear enough in real applications [19]. The proposed hybrid approach contains four steps: first, the BHHO selects informative genes; second, the PCA reduces the number of selected genes compatible with the number of qubits in the quantum device; third, the SMOTE method is used to handle the imbalanced classification problem; and fourth, the quantum kernel SVM is applied for the gene classification.…”

Section: The Proposed Approachmentioning

confidence: 99%

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An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks

Houssein,

Abohashima,

Elhoseny

et al. 2022

Preprint

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Cancer classification based on gene expression increases early diagnosis and recovery, but high-dimensional genes with a small number of samples are considered the major challenge in cancer classification tasks. Gene selection is a critical step in large-dimensional features to select the most informative and relevant subset of genes. The Gene selection problem utilizes metaheuristic techniques to extract the optimal subset of genes in microarray cancer datasets, such as particle swarm optimization (PSO), Harris hawks optimization (HHO), and grey wolf optimizer (GWO). Binary Harris hawk optimization (BHHO) technique, which mimics the behavior of the cooperative action of Harris hawks in nature, is recently proposed as one of these techniques. The biggest challenge of quantum hardware is the limited number of qubits, which restricts the use of quantum devices in real applications. The principal component analysis (PCA) is applied to reduce the selected genes to match the qubit numbers. In this work, a new hybrid quantum-kernel support vector machine (QKSVM) with BHHO called BHHO-PCA-QKSVM for cancer classification on a quantum simulator. This study aims to improve the microarray cancer prediction performance with the quantum kernel estimation based on the informative genes by BHHO. The quantum computer is used for estimation the kernel with the training data of the reduced genes and generation of the quantum kernel matrix. Besides, the classical computer is used for drawing the support vectors based on the quantum kernel matrix and applying the prediction stage. The colon and breast microarray datasets are used for evaluating the proposed approach performance with all genes and the selected genes. The proposed model enhances the overall performance of the two datasets. Also, the final results of the proposed model compared with different quantum feature maps (kernels) and classical radial basis function (RBF) kernel.

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Section: Discussionmentioning

confidence: 99%

Section: The Proposed Approachmentioning

confidence: 99%

An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks

Houssein,

Abohashima,

Elhoseny

et al. 2022

Preprint

View full text Add to dashboard Cite

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Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer

Bhatia

Phillipson

2021

Computational Science – ICCS 2021

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Quantum Computing in Telecommunication—A Survey

Phillipson

2023

Mathematics

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Quantum computing, an emerging paradigm based on the principles of quantum mechanics, has the potential to revolutionise various industries, including Telecommunications. This paper explores the transformative impact of quantum computing on the telecommunication market, focusing on its applications in solving computationally intensive problems. By leveraging the inherent properties of quantum systems, such as superposition and entanglement, quantum computers offer the promise of exponential computational speedup and enhanced problem-solving capabilities. This paper provides an in-depth analysis of the current state of quantum computing in telecommunication, examining key algorithms and approaches, discussing potential use cases, and highlighting the challenges and future prospects of this disruptive technology.

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Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection

Cited by 3 publications

References 31 publications

An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks

An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks

Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer

Quantum Computing in Telecommunication—A Survey

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