Following Decoding Small Surface Codes with Feedforward Neural Networks, I am trying to train a similar neural network decoder with
Decoding is reduced to a classification problem, by decomposing the error $E$ into $$E = SCL$$
where $S$ is a stabilizer, $C$ pure Error, and $L$ logical operator. A pure error decoder is used to find a plausible pure error $C$, and the network should predict the remaining logical operator $L$.
stim, it is straightforward to sample syndrome measurements and logical observable from the circuit.
It seems that to find the correct logical operator (serving as the training label) given the predicted pure error $C$, syndromes, and logical observable measured at the end of the circuit, I need to access the data qubits, which is not possible based on this answer.
I wanted to double-check if the
stim::FrameSimulator suggested in the answer above, is the way to go, or if I misunderstood the training procedure?