I'm now studying quantum machine learning. While studying papers about quantum machine learning, I have a question about quantum embedding. To my knowledge, some general embedding algorithms, such as basis encoding, phase-encoding, and amplitude encoding, encode classical bits into qubits.
However, in many papers leveraging quantum ML, they mention i) "$\textbf{Encode pixels as angles}$" and ii) encode the qubits with the Encoder with several rotation gates $R_x$,$R_y$,$R_z$ and denote the initial quantum state as $|000..\rangle$, $\textit{i.e.}$, several wires with $|0\rangle$ in the figure.
Assume pixels as classical data; I'm confused that i) refers to basis encoding. If so, why is the initial state $|000..\rangle$ rather than $|xxx..\rangle$, where $x \in [0,1]$? In addition, if the encoder conducts the encoding that I illustrated above, are these QML encodes the classical data twice?
I found the above mention in the paper "[Variational Quantum Pulse Learning]"
+) Edit
To understand my question, I'm studying various quantum ML papers and I find that It seems the Encoder in Quantum ML papers is not about embedding (classical bits to qubits). Many descriptions of the encoder are activated on qubits. If so, are these encoders just a part of variational quantum circuits?