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With the advent of real-world quantum computing, the idea that parametrized quantum computations can be used as hypothesis families in a quantum-classical machine learning system is gaining increasing traction.
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Lilian Weng · 2018
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Google · 2018
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Evan Peters, Joao Caldeira, Alan Ho, Stefan Leichenauer, Masoud Mohseni, Hartmut Neven, Panagiotis Spentzouris, Doug Strain, and Gabriel N Perdue · 2021
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A rigorous and robust quantum speed-up in supervised machine learning
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Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, and Jens Eisert · 2021
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Sofiene Jerbi, Lea M. Trenkwalder, Hendrik Poulsen Nautrup, Hans J. Briegel, and Vedran Dunjko · 2021
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Quantum agents in the gym: a variational quantum algorithm for deep q-learning
Andrea Skolik, Sofiene Jerbi, and Vedran Dunjko · 2021
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Parametrized quantum circuits for reinforcement learning
TensorFlow Quantum · 2021
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Effect of data encoding on the expressive power of variational quantum-machine-learning models
Maria Schuld, Ryan Sweke, and Johannes Jakob Meyer · 2021
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Explainable ai: A review of machine learning interpretability methods
Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis · 2021
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Universal approximation property of quantum machine learning models in quantum-enhanced feature spaces
Takahiro Goto, Quoc Hoan Tran, and Kohei Nakajima · 2021
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One qubit as a universal approximant
Adrián Pérez-Salinas, David López-Núñez, Artur García-Sáez, Pol Forn-Díaz, and José I Latorre · 2021
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