Support vector machines on the D-wave quantum annealer

DOI10.1016/j.cpc.2019.107006OpenAlexW2982852456WikidataQ126835806 ScholiaQ126835806MaRDI QIDQ2698831

Yanyan Li

Publication date: 25 April 2023

Published in: Computer Physics Communications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1906.06283

zbMATH Keywords

classificationquantum computationmachine learningsupport vector machinequantum annealingkernel-based SVM

Mathematics Subject Classification ID

Biology and other natural sciences (92-XX) Computer science (68-XX)

Related Items (5)

Applications and Computational Advances for Solving the QUBO Model ⋮ QSurfNet: a hybrid quantum convolutional neural network for surface defect recognition ⋮ Balanced \(k\)-means clustering on an adiabatic quantum computer ⋮ Garden optimization problems for benchmarking quantum annealers ⋮ Benchmarking advantage and D-wave 2000Q quantum annealers with exact cover problems

Uses Software

• GitHub

Cites Work

• Bagging predictors
• Minor-embedding in adiabatic quantum computation. I: The parameter setting problem
• Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
• Beitrag zur Theorie des Ferromagnetismus
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