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Recently, quantum neural networks or quantum-classical neural networks (qcNN) have been actively studied, as a possible alternative to the conventional classical neural network (cNN), but their practical and theoretically-guaranteed performance is still to be investigated.
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A Mari, T. R Bromley, J Izaac, M Schuld and N Killoran · 2020
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Generalization error bounds of gradient descent for learning over-parameterized deep relu networks
Y Cao and Q Gu · 2020
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Predicting many properties of a quantum system from very few measurements
H.-Y Huang, R Kueng and J Preskill · 2020
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F. J Gil Vidal and D. O Theis · 2020
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A Abbas, D Sutter, C Zoufal, A Lucchi, A Figalli and S Woerner · 2021
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S Thanasilp, S Wang, M Cerezo and Z Holmes · 2022
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Laziness, barren plateau, and noise in machine learning
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X Wang, J Liu, T Liu, Y Luo, Y Du and D Tao · 2022
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H.-Y Huang, R Kueng, G Torlai, V. V Albert and J Preskill · 2022
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Representation theory for geometric quantum machine learning
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Quantum fisher kernel for mitigating the vanishing similarity issue
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Analytic theory for the dynamics of wide quantum neural networks
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Quantum Phase Recognition via Quantum Kernel Methods
Y Wu, B Wu, J Wang and X Yuan · 2023
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