Quantum Kernel Evaluation via Hong-Ou-Mandel Interference

One of the fastest growing areas of interest in quantum computing is its application to, and unification with machine learning methods. One of the most popular applications in quantum machine learning methods are quantum kernels methods. Evaluating quantum kernels can be experimentally difficult as it requires measuring the overlap of two quantum states. In this article we propose an experimentally realisable application of kernel evaluation using temporally encoded photons and Hong-Ou-Mandel (HOM) interference. We present a complete description of this method and show that it can be combined with kernel mean embedding and maximum mean discrepancy to efficiently classify encoded data.