Make as many non-executable demos executable as possible

[josh146]

Currently, several demos are not executed when the website is deployed, for a couple of reasons:

• Execution time: qgrnn, qonn, quantum_neural_net

• Requires access to hardware/external services or APIs: pytorch_noise, quantum_volume

• Tricky to install dependency on CircleCI: qsim_beyond_classical

We should attempt to make as many executable as possible, as we will otherwise be unaware if they no longer work with the latest PL version. Of the above, it is likely that pytorch_noise and quantum_neural_net no longer work.

For the ones with currently a slow execution time, perhaps porting them to use backpropagation (rather than parameter-shift) will be beneficial, and allow them to be executable.

Note: for quantum_neural_net, it should be updated to use strawberryfields.tf and the CVQNN layers (rather than hand-coding the layers manually)

Todo:

• Check if pytorch_noise still works
• Check if quantum_neural_net still works
• Install qsim on CircleCI
• See if the slow demos can be made faster using backprop etc.

[KetpuntoG]

These are the three demos that we can currently try to make executable:

• function_fitting_qsp.py
• learning2learn.py (pending of a bug fix)
• qnspsa.py