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- W3178462323 abstract "Abstract The data-embedding process is one of the bottlenecks of quantum machine learning, potentially negating any quantum speedups. In light of this, more effective data-encoding strategies are necessary. We propose a photonic-based bosonic data-encoding scheme that embeds classical data points using fewer encoding layers and circumventing the need for nonlinear optical components by mapping the data points into the high-dimensional Fock space. The expressive power of the circuit can be controlled via the number of input photons. Our work sheds some light on the unique advantages offered by quantum photonics on the expressive power of quantum machine learning models. By leveraging the photon-number dependent expressive power, we propose three different noisy intermediate-scale quantum-compatible binary classification methods with different scaling of required resources suitable for different supervised classification tasks." @default.
- W3178462323 created "2021-07-19" @default.
- W3178462323 creator A5008072523 @default.
- W3178462323 creator A5010702762 @default.
- W3178462323 creator A5056329088 @default.
- W3178462323 date "2022-06-20" @default.
- W3178462323 modified "2023-10-17" @default.
- W3178462323 title "Fock state-enhanced expressivity of quantum machine learning models" @default.
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- W3178462323 doi "https://doi.org/10.1140/epjqt/s40507-022-00135-0" @default.
- W3178462323 hasPublicationYear "2022" @default.
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