Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/4515EAC3-A012-49E4-9D7A-4D2F0C81D639 | - |
| Identifier | https://doi.org/10.1140/epjqt/s40507-022-00135-0 | - |
| Identifier | https://epjquantumtechnology.springeropen.com/articles/10.1140/epjqt/s40507-022-00135-0 | - |
| Language | en | - |
| Extent | 23 pages | en |
| Title | Fock state-enhanced expressivity of quantum machine learning models | en |
| Creator | Gan Beng Yee | en |
| Creator | Leykam Daniel | en |
| Creator | Angelakis Dimitrios | en |
| Creator | Αγγελακης Δημητριος | el |
| Publisher | Springer | en |
| Content Summary | 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. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2023-07-19 | - |
| Date of Publication | 2022 | - |
| Subject | Quantum physics | en |
| Subject | Machine learning | en |
| Subject | Quantum photonics | en |
| Bibliographic Citation | B. Y. Gan, D. Leykam, and D. G. Angelakis, “Fock state-enhanced expressivity of quantum machine learning models,” EPJ Quantum Technol., vol. 9, no. 1, June 2022, doi: 10.1140/epjqt/s40507-022-00135-0. | en |
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