Unlike amplitude encoding, which has a fixed method of implementation, angle encoding lacks a standardized approach. In relation to the angle encoding technique specified in PennyLane, is there a particular research paper that influenced its configuration? What is the rationale behind the design of such angle encoding, and how do various angle encoding strategies impact the model’s performance? Furthermore, are there established metrics or criteria to evaluate the effectiveness or quality of a specific angle encoding method?
Hi @zj-lucky,
The qml.AngleEmbedding template in PennyLane was designed to give you the flexibility of choosing the type of embedding you want while making it easy to use this embedding in a single line of code. There may have been additional considerations too (I didn’t add this to PennyLane myself so I wouldn’t really know).
Knowing how embeddings affect model performance is still an active research question so the answer will depend on your specific use-case. There’s no universally-better embedding yet that’s known to be better than the others for all use-cases.
How to measure performance depends on what you want to do. I definitely recommend that you read this blog by Maria Schuld to get a sense of some measures of performance and why they’re good and not so good.
I hope this helps you answer these very interesting questions!
Thank you very much for your response. In our new question, we also touch upon angle encoding, but this new issue primarily focuses on the gradient in quantum neural networks. Could you and your team assist us in solving this new problem? It has troubled us for a long time, and I posted it on this platform yesterday. Thank you very much for your help.
The efficient angle encoding scheme for a sequence of real values can be implemented using QCrank encoder. For a binary input there is a QBArt encoding scheme, both described in this paper, which is still under review. The important feature of both encoding is we give explicit construction of gate based circuit, no need for numerical circuit synthesis - if you want to run it on a real HW.