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• W3075559820 abstract "Quantum computers can be used for supervised learning by treating parametrized quantum circuits as models that map data inputs to predictions. While a lot of work has been done to investigate the practical implications of this approach, many important theoretical properties of these models remain unknown. Here, we investigate how the strategy with which data are encoded into the model influences the expressive power of parametrized quantum circuits as function approximators. We show that one can naturally write a quantum model as a partial Fourier series in the data, where the accessible frequencies are determined by the nature of the data-encoding gates in the circuit. By repeating simple data-encoding gates multiple times, quantum models can access increasingly rich frequency spectra. We show that there exist quantum models which can realize all possible sets of Fourier coefficients, and therefore, if the accessible frequency spectrum is asymptotically rich enough, such models are universal function approximators." @default.
• W3075559820 created "2020-08-24" @default.
• W3075559820 creator A5073832417 @default.
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• W3075559820 date "2021-03-24" @default.
• W3075559820 modified "2023-10-17" @default.
• W3075559820 title "Effect of data encoding on the expressive power of variational quantum-machine-learning models" @default.
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• W3075559820 doi "https://doi.org/10.1103/physreva.103.032430" @default.
• W3075559820 hasPublicationYear "2021" @default.
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