Experimental kernel-based quantum machine learning in finite feature space

Understand

We implement an all-optical setup demonstrating kernel-based quantum machine learning for two-dimensional classification problems.

• In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while the model training is processed on a classical computer.
• Our two-photon proposal encodes data points in a discrete, eight-dimensional feature Hilbert space.
• In order to maximize the application range of the deployable kernels, we optimize feature maps towards the resulting kernels' ability to separate points, i.e., their resolution, under the constraint of finite, fixed Hilbert space dimension.