Quantum machine learning offers exciting opportunities to learn faster with fewer data. However, finding the right models and applications is as much a science as an art. There is a rich theory on generalisation, expressivity, and learnability from computer sciences, and physics. However, the terminology can be confusing, and restricting to what’s provable can be severely limiting.
Therefore, in this talk, we’ll cover both the theory and best practices in quantum machine learning.
About Julian Velzen
Julian likes to pioneer. Equipped with a master degree in physics, he put Capgemini's quantum technology efforts on the map, and now leads the computing futures (bits/qubits/neurons) domain from within the group's CTIO++ community. Furthermore, he initiated and led project FARM, a big data solution for small-holder farmers in developing countries.
More on Julian Velzen.