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Ιδρυματικό Αποθετήριο [SANDBOX]
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI: | http://purl.tuc.gr/dl/dias/4515EAC3-A012-49E4-9D7A-4D2F0C81D639 | ||
| Έτος | 2022 | ||
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | 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. https://doi.org/10.1140/epjqt/s40507-022-00135-0 | ||
| Εμφανίζεται στις Συλλογές |
The data-embedding process is one of the bottlenecks of quantum machine learning, potentially negating any quantum speedups. In light of this, more effectivedata-encoding strategies are necessary. We propose a photonic-based bosonicdata-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-scalequantum-compatible binary classification methods with different scaling of requiredresources suitable for different supervised classification tasks.
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