Hey Pennylane Team , I was going through the example notebooks and specifically the Example Q3 - Variational Classifier and I wanted to understand the correct way to adapt this script to be able to predict the Iris dataset for multi-classes. I know in this paper it mentions that these circuit-centric quantum classifiers could be operated as a multi-class classifier, but it only does a “one-versu-all” binary discrimination subtask. I did not see any other examples that tried to do this so I wanted to get some feedback on the approach I was thinking.

We return a tuple of the measurements of all the wires from the circuit

(qml.expval.PauliZ(0), qml.expval.PauliZ(1))

Passing these values through the np.sign(measurements) we would have 4 possibilities that map to 4 classes ([0,0], [0,1], [1,0], [1,1]) then that error would be passed through cross entropy and optimized with the one step. Although, this means that we are only able to classify 2**n amount of classes as an upper limit. Is there a more scalable way to implement a multi-class classification or are we limited to the number of qubits just as how we are limited to the number of features we can amplitube encode based on the number of qubits. Any insight would greatly help. Thanks!