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 shed some light on the unique advantages offers 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.
@article{arxiv.2107.05224,
title = {Fock State-enhanced Expressivity of Quantum Machine Learning Models},
author = {Beng Yee Gan and Daniel Leykam and Dimitris G. Angelakis},
journal= {arXiv preprint arXiv:2107.05224},
year = {2022}
}