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Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification.
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2019
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Ewin Tang, “A quantum-inspired classical algorithm for recommendation systems,” in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (2019) pp. 217–228
2019
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2019
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Tobias Stollenwerk, Bryan O’Gorman, Davide Venturelli, Salvatore Mandrà, Olga Rodionova, Hokkwan Ng, Banavar Sridhar, Eleanor Gilbert Rieffel, and Rupak Biswas, “Quantum annealing applied to de-conflicting optimal trajectories for air traffic management,” IEEE transactions on intelligent transportation systems (2019)
2019
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2019
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2019
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Frédéric Bapst, Wahid Bhimji, Paolo Calafiura, Heather Gray, Wim Lavrijsen, Lucy Linder, and Alex Smith, “A pattern recognition algorithm for quantum annealers,” Computing and Software for Big Science 4
2019
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Adam Pearson, Anurag Mishra, Itay Hen, and Daniel A. Lidar, “Analog errors in quantum annealing: doom and hope,” npj Quantum Information 5
2019
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Alex Mott, Joshua Job, Jean-Roch Vlimant, Daniel Lidar, and Maria Spiropulu, “Kinematics variables of higgs and background data,” (2020)
2020
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