11.3 Hybrid architectures in Qiskit
In the previous section, we discussed how hybrid QNNs could be implemented and trained using PennyLane in conjunction with TensorFlow, an ML framework that we already know how to use. We will devote this section to studying how to work with these hybrid architectures in Qiskit, and in this mission we will need to face a new challenge.
For better or for worse, Qiskit doesn’t have a built-in TensorFlow interface at the time of writing. It only has native support for a different ML framework: PyTorch. So, if we want to get those hybrid NNs working on Qiskit, we better learn a thing or two about PyTorch. As daunting as this task may seem, it won’t be such a hassle and it will greatly pay off in the future — and, yes, the future is our next chapter on QGANs.
Important note
We will be using version 1.13 of the PyTorch package. If you are using a different version, things may be slightly different!
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