Advanced use of QuASK#
These series of tutorial dig in the advanced feature of quask. In the first tutorial we show how to make quask rely on one of the existing backends (Qiskit, Pennylane, Braket, Qibo, …) as well as how to define a custom backend. Then, we will see particular feature that are not available on most quantum machine learning frameworks. The second tutorial shows how to evaluate a quantum kernel according to several different criterias, and these can be used to diagnose poor performances of the model or justify good ones.
Note
quask is an ongoing project. If you need a certain functionality to be supported or modified, contact us to see if it can be quickly integrated.