Parameterized Quantum Circuits with Quantum Kernels for Machine
Learning: A Hybrid Quantum-Classical Approach

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Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach

28 September 2022

Daniel T. Chang

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Papers citing "Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach"

9 / 9 papers shown

Title
Exponential concentration in quantum kernel methods Supanut Thanasilp Samson Wang M. Cerezo Zoë Holmes 81 89 0 23 Aug 2022
Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning Francesco Di Marcantonio Massimiliano Incudini Davide Tezza Michele Grossi 120 15 0 30 Jun 2022
Quantum machine learning beyond kernel methods Sofiene Jerbi Lukas J. Fiderer Hendrik Poulsen Nautrup Jonas M. Kubler Hans J. Briegel Vedran Dunjko 76 173 0 25 Oct 2021
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey Benyamin Ghojogh A. Ghodsi Fakhri Karray Mark Crowley 65 37 0 15 Jun 2021
Supervised quantum machine learning models are kernel methods Maria Schuld 153 372 0 26 Jan 2021
A rigorous and robust quantum speed-up in supervised machine learning Yunchao Liu Srinivasan Arunachalam K. Temme 113 547 0 05 Oct 2020
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences J. E. Johnson Valero Laparra Adrián Pérez-Suay Miguel D. Mahecha Gustau Camps-Valls 23 25 0 29 Jul 2020
Parameterized quantum circuits as machine learning models Marcello Benedetti Erika Lloyd Stefan H. Sack Mattia Fiorentini 112 901 0 18 Jun 2019
Supervised learning with quantum enhanced feature spaces Vojtěch Havlíček A. Córcoles K. Temme A. Harrow A. Kandala J. Chow J. Gambetta 88 1,836 0 30 Apr 2018