I'm new in the area and I'm quite curious.
- What are the main advantages of quantum machine learning over "classical" machine learning (from sklearn library, for example)?
- Is just only a boost to the computational process or it could be a way for building better models (in term of bias/variance trade-off - more generalized model)?
- Are the super-positions (and other quantum characteristics) a way for capturing better statistical properties (so, maybe, quantum computation can be used in a sort of "features engineering" step)?
thanks everyone!
