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- W2964434046 abstract "Quantum algorithms can enhance machine learning in different aspects. Here, we study quantum-enhanced least-square support vector machine (LS-SVM). Firstly, a novel quantum algorithm that uses continuous variable to assist matrix inversion is introduced to simplify the algorithm for quantum LS-SVM, while retaining exponential speed-up. Secondly, we propose a hybrid quantum-classical version for sparse solutions of LS-SVM. By encoding a large dataset into a quantum state, a much smaller transformed dataset can be extracted using quantum matrix toolbox, which is further processed in classical SVM. We also incorporate kernel methods into the above quantum algorithms, which uses both exponential growth Hilbert space of qubits and infinite dimensionality of continuous variable for quantum feature maps. The quantum LS-SVM exploits quantum properties to explore important themes for SVM such as sparsity and kernel methods, and stresses its quantum advantages ranging from speed-up to the potential capacity to solve classically difficult machine learning tasks." @default.
- W2964434046 created "2019-08-13" @default.
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- W2964434046 date "2020-09-01" @default.
- W2964434046 modified "2023-10-16" @default.
- W2964434046 title "Quantum-enhanced least-square support vector machine: Simplified quantum algorithm and sparse solutions" @default.
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- W2964434046 doi "https://doi.org/10.1016/j.physleta.2020.126590" @default.
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