Fock State-enhanced Expressivity of Quantum Machine Learning Models. (arXiv:2107.05224v2 [quant-ph] UPDATED)

Web: http://arxiv.org/abs/2107.05224

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 …

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