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Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data.
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“Support vector method for novelty detection”
Bernhard Schölkopf et al · 1999
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“Support vector method for novelty detection”
Bernhard Schölkopf et al · 1999
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“Support vector method for novelty detection”
Bernhard Schölkopf et al · 1999
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Nello Cristianini et al · 2001
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“On kernel-target alignment”
Nello Cristianini et al · 2001
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“Efficient solvability of Hamiltonians and limits on the power of some quantum computational models”
Rolando Somma et al · 2006
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“Efficient solvability of Hamiltonians and limits on the power of some quantum computational models”
Rolando Somma et al · 2006
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“Support Vector Machines”
Ingo Steinwart and Andreas Christmann · 2008
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“The anti- k t k_{t} jet clustering algorithm”
M. Cacciari · 2008
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“Support Vector Machines”
Ingo Steinwart and Andreas Christmann · 2008
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“The anti- k t k_{t} jet clustering algorithm”
M. Cacciari · 2008
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“Algorithms for learning kernels based on centered alignment”
Corinna Cortes et al · 2012
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“Algorithms for learning kernels based on centered alignment”
Corinna Cortes et al · 2012
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“Generating a state t-design by diagonal quantum circuits”
Yoshifumi Nakata et al · 2014
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“DELPHES 3, A modular framework for fast simulation of a generic collider experiment”
J. de Favereau · 2014
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“Generating a state t-design by diagonal quantum circuits”
Yoshifumi Nakata et al · 2014
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“DELPHES 3, A modular framework for fast simulation of a generic collider experiment”
J. de Favereau · 2014
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“An Introduction to PYTHIA 8.2”
Torbjörn Sjöstrand · 2015
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“An Introduction to PYTHIA 8.2”
Torbjörn Sjöstrand · 2015
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“Neural architecture search with reinforcement learning”
Barret Zoph and Quoc Le · 2016
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“Neural architecture search with reinforcement learning”
Barret Zoph and Quoc Le · 2016
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“Quantum machine learning”
Jacob Biamonte et al · 2017
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“Kernel mean embedding of distributions: A review and beyond”
Krikamol Muandet et al · 2017
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“Hierarchical representations for efficient architecture search”
Hanxiao Liu et al · 2017
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“Quantum machine learning”
Jacob Biamonte et al · 2017
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“Kernel mean embedding of distributions: A review and beyond”
Krikamol Muandet et al · 2017
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“Hierarchical representations for efficient architecture search”
Hanxiao Liu et al · 2017
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“Quantum computing in the NISQ era and beyond”
John Preskill · 2018
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“Quantum computing in the NISQ era and beyond”
John Preskill · 2018
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“Quantum Machine Learning in Feature Hilbert Spaces”
Maria Schuld and Nathan Killoran · 2019
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“Supervised learning with quantum-enhanced feature spaces”
Vojtech Havlicek et al · 2019
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“Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms”
Sukin Sim et al · 2019
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“Opytimizer: A nature-inspired python optimizer”
Gustavo de Rosa et al · 2019
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“Quantum Machine Learning in Feature Hilbert Spaces”
Maria Schuld and Nathan Killoran · 2019
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“Supervised learning with quantum-enhanced feature spaces”
Vojtech Havlicek et al · 2019
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“Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms”
Sukin Sim et al · 2019
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“Opytimizer: A nature-inspired python optimizer”
Gustavo de Rosa et al · 2019
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“Asymptotic learning curves of kernel methods: empirical data versus teacher–student paradigm”
Stefano Spigler et al · 2020
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“Automated machine learning: Review of the state-of-the-art and opportunities for healthcare”
Jonathan Waring et al · 2020
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“AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity”
Silviu-Marian Udrescu et al · 2020
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“Quantum embeddings for machine learning”
Seth Lloyd et al · 2020
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“Asymptotic learning curves of kernel methods: empirical data versus teacher–student paradigm”
Stefano Spigler et al · 2020
Cited alongside, same era.
“Automated machine learning: Review of the state-of-the-art and opportunities for healthcare”
Jonathan Waring et al · 2020
Cited alongside, same era.
“AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity”
Silviu-Marian Udrescu et al · 2020
Cited alongside, same era.
“Robust resource-efficient quantum variational ansatz through an evolutionary algorithm”
Yuhan Huang et al · 2022
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“Quantum circuit architecture search for variational quantum algorithms”
Yuxuan Du et al · 2022
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“Genetically auto-generated quantum feature maps”
Bang-Shien Chen and Jann-Long Chern · 2022
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“Deep learning, reinforcement learning, and world models”
Yutaka Matsuo et al · 2022
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“Challenges and opportunities in quantum machine learning”
Marco Cerezo et al · 2022
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“Diagnosing barren plateaus with tools from quantum optimal control”
Martin Larocca et al · 2022
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Seth Lloyd et al · 2020
Cited alongside, same era.
“A rigorous and robust quantum speed-up in supervised machine learning”
Yunchao Liu et al · 2021
Cited alongside, same era.
“The inductive bias of quantum kernels”
Jonas Kübler et al · 2021
Cited alongside, same era.
“Power of data in quantum machine learning”
Hsin-Yuan Huang et al · 2021
Cited alongside, same era.
“Geometric deep learning: Grids, groups, graphs, geodesics, and gauges”
Michael Bronstein et al · 2021
Cited alongside, same era.
“Machine learning with quantum computers”
Maria Schuld and Francesco Petruccione · 2021
Cited alongside, same era.
“Reinforcement learning for optimization of variational quantum circuit architectures”
Mateusz Ostaszewski et al · 2021
Cited alongside, same era.
“Deep learning, reinforcement learning, and world models”
Yutaka Matsuo et al · 2022
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“On scientific understanding with artificial intelligence”
Mario Krenn et al · 2022
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“Robust resource-efficient quantum variational ansatz through an evolutionary algorithm”
Yuhan Huang et al · 2022
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“Quantum circuit architecture search for variational quantum algorithms”
Yuxuan Du et al · 2022
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“Genetically auto-generated quantum feature maps”
Bang-Shien Chen and Jann-Long Chern · 2022
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“Deep learning, reinforcement learning, and world models”
Yutaka Matsuo et al · 2022
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“Higher-Order Topological Kernels via Quantum Computation”
M. Incudini et al · 2023
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“Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines”
Jonas Jäger and Roman Krems · 2023
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“Quantum anomaly detection in the latent space of proton collision events at the LHC”
Kinga Woźniak et al · 2023
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“A Unified Framework for Trace-induced Quantum Kernels”
Beng Gan et al · 2023
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“Bandwidth Enables Generalization in Quantum Kernel Models”
Abdulkadir Canatar et al · 2023
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“Representation Theory for Geometric Quantum Machine Learning”
Michael Ragone et al · 2023
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“Quantum Information Science”
Riccardo Manenti and Mario Motta · 2023
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“Unravelling physics beyond the standard model with classical and quantum anomaly detection”
Julian Schuhmacher et al · 2023
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“Dataset for Quantum anomaly detection in the latent space of proton collision events at the LHC”
Maurizio Pierini and Kinga Wozniak · 2023
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“Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning”
Francesco Di et al · 2023
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“Dataset for Quantum anomaly detection in the latent space of proton collision events at the LHC”
Maurizio Pierini and Kinga Wozniak · 2023
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“Quantum anomaly detection in the latent space of proton collision events at the LHC”
Kinga Woźniak et al · 2023
Closest in time.
“Higher-Order Topological Kernels via Quantum Computation”
M. Incudini et al · 2023
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“Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines”
Jonas Jäger and Roman Krems · 2023
Closest in time.
“Quantum anomaly detection in the latent space of proton collision events at the LHC”
Kinga Woźniak et al · 2023
Closest in time.
“A Unified Framework for Trace-induced Quantum Kernels”
Beng Gan et al · 2023
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“Bandwidth Enables Generalization in Quantum Kernel Models”
Abdulkadir Canatar et al · 2023
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“Representation Theory for Geometric Quantum Machine Learning”
Michael Ragone et al · 2023
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“Quantum Information Science”
Riccardo Manenti and Mario Motta · 2023
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“Unravelling physics beyond the standard model with classical and quantum anomaly detection”
Julian Schuhmacher et al · 2023
Closest in time.
“Dataset for Quantum anomaly detection in the latent space of proton collision events at the LHC”
Maurizio Pierini and Kinga Wozniak · 2023
Closest in time.
“Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning”
Francesco Di et al · 2023
Closest in time.
“Dataset for Quantum anomaly detection in the latent space of proton collision events at the LHC”
Maurizio Pierini and Kinga Wozniak · 2023
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“Quantum anomaly detection in the latent space of proton collision events at the LHC”
Kinga Woźniak et al · 2023
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“Exponential concentration in quantum kernel methods”
Supanut Thanasilp et al · 2024
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“Toward Useful Quantum Kernels”
Massimiliano Incudini et al · 2024
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“Exponential concentration in quantum kernel methods”
Supanut Thanasilp et al · 2024
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“Toward Useful Quantum Kernels”
Massimiliano Incudini et al · 2024
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