Hi @BillWisotsky, I’m glad it’s going well so far!
I agree that having a ‘plug and play’ example would be great. I will note it down as something we might want to build in the near future. Thank you for suggesting it.
Embedding data is one very big question in quantum machine learning and it’s one of the steps that currently generate very big overheads. You can learn about quantum embedding from this section of our quantum glossary.
Aside from the two examples described there I also recommend using AngleEmbedding, which can sometimes be easier to understand. You can also find more embedding templates in the PennyLane documentation.
The main reason why you would choose one over the other is because of the kind of data you have. If you have binary data then BasisEmbedding is well suited for that. If your data are real numbers between 0 and 2pi then AngleEmbedding is a great choice. If your data can be pre-processed to fit into one of these cases then that’s a good thing you can do too.
If you’re using continuous-variable circuits then you might want to use displacement embedding or squeezing embedding.
The QAOAEmbedding includes both the data embedding and also the ansatz, so it can be more tricky to understand but it can also be very useful once you do.
If you have more information about the kind of data that you have then I can suggest one or another method for embedding.
I hope this helps!