So there are a lot of feature mapping techniques out there for Quantum Machine Learning, but I'm not sure which one to use for my next VQC.
Can anyone explain when and why to use each of the following?
- Amplitude embedding
- Basis embedding
- Angle embedding
Although I understand the difference in the architecture of each method, I don't see why you would choose one over another.