This lecture is organised as part of the CERN QTI online lecture series.

The representation of data is of paramount importance for machine learning methods. Kernel methods are used to enrich the feature representation, allowing better generalisation. Quantum kernels implement efficiently complex transformation encoding classical data in the Hilbert space of a quantum system, resulting in even exponential speedup. However, we need prior knowledge of the data to choose an appropriate parametric quantum circuit that can be used as quantum embedding.

In this talk, the authors will propose an algorithm that automatically selects the best quantum embedding through a combinatorial optimisation procedure that modifies the structure of the circuit, changing the generators of the gates, their angles (which depend on the data points), and the qubits on which the various gates act.

For more details, visit: https://indico.cern.ch/event/1251853/.

The recording of this talk is now available at: https://www.youtube.com/watch?v=nCpWgBm4gKY