Assistance regarding implementing qutrit measurements into pennylane model.
Expectation values in the Z basis
exp_vals = [qml.expval(qml.PauliZ(position)) for position in range(n_qubits)]
return tuple(exp_vals)
Source:
Thank you.
Hey @kevinkawchak! Welcome to the forum 
You can see a complete list of qutrit operations that are available here:
https://docs.pennylane.ai/en/stable/introduction/operations.html?highlight=qutrits#qutrit-operators
You might also find this demo useful:
Let me know if that helps!
Thank you @isaacdevlugt,
I was able to run @Guillermo_Alonso’s demo modifying Tshift gates to obtain predictable sets of ternary outputs. What steps would I need to take to implement a user dataset for a classification task, coming from a deep learning port perspective?
-Kevin
Nice! The steps you’d need to take to train a qutrit circuit for a classification task will be extremely similar to what’s in our variational classifier demo. Let me know if that helps!