  One of the fastest growing areas of interest in quantum computing is its use
within machine learning methods, in particular through the application of
quantum kernels. Despite this large interest, there exist very few proposals
for relevant physical platforms to evaluate quantum kernels. In this article,
we propose and simulate a protocol capable of evaluating quantum kernels using
Hong-Ou-Mandel (HOM) interference, an experimental technique that is widely
accessible to optics researchers. Our proposal utilises the orthogonal temporal
modes of a single photon, allowing one to encode multi-dimensional feature
vectors. As a result, interfering two photons and using the detected
coincidence counts, we can perform a direct measurement and binary
classification. This physical platform confers an exponential quantum advantage
also described theoretically in other works. We present a complete description
of this method and perform a numerical experiment to demonstrate a sample
application for binary classification of classical data.
