  Recently, machine learning had a remarkable impact, from scientific to
everyday-life applications. However, complex tasks often imply unfeasible
energy and computational power consumption. Quantum computation might lower
such requirements, although it is unclear whether enhancements are reachable by
current technologies. Here, we demonstrate a kernel method on a photonic
integrated processor to perform a binary classification. We show that our
protocol outperforms state-of-the-art kernel methods including gaussian and
neural tangent kernels, exploiting quantum interference, and brings a smaller
improvement also by single photon coherence. Our scheme does not require
entangling gates and can modify the system dimension through additional modes
and injected photons. This result opens to more efficient algorithms and to
formulating tasks where quantum effects improve standard methods.
