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The core of quantum machine learning is to devise quantum models with good trainability and low generalization error bound than their classical counterparts to ensure better reliability and interpretability.
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A grover-search based quantum learning scheme for classification
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, and Dacheng Tao · 2021
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Leonardo Banchi, Jason Pereira, and Stefano Pirandola · 2021
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An efficient measure for the expressivity of variational quantum algorithms
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Hsin-Yuan Huang, Richard Kueng, and John Preskill · 2021
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Connecting ansatz expressibility to gradient magnitudes and barren plateaus
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Quantum machine learning models are kernel methods
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https://quantum-computing.ibm.com/ , 2021
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Encoding-dependent generalization bounds for parametrized quantum circuits, 2021
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Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms
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