Hey @Aadi_Tiwari! Welcom to the forum !

Really cool project! What I recommend doing is to start simpler. Based on your notebook, it looks like you’re trying to implement this paper. The recurrent unit is relatively complicated!

What I suggest for a “simpler” QRNN is as follows. You can keep the same circuit design as in the paper referenced above, but the bulk of your RNN recurrent unit can literally just be that circuit, where the measurements are, say, Pauli Z expectations in your embedded space. Then, you can have a classical processing layer (a softmax layer) to turn the measurement data from your quantum RNN into a probability vector (the final output of your recurrent cell).

On paper this should work! Try this out and see if you can get that *functioning* (it may not be very “trainable”, per se, but this simpler algorithm should still have the same properties of any other RNN).