Trainable flag for parametrized gates

[stavros11]

This PR adds the trainable flag in all parametrized gates which allows the user to hide them from circuit.get_parameters() and circuit.set_parameters(). For example

c = Circuit(3)
c.add(gates.RX(0, theta=0))
c.add(gates.RY(1, theta=0.5, trainable=False))
c.add(gates.RZ(2, theta=0.8))

print(c.get_parameters()) 
# will print [0, 0.8] ignoring the RY

c.set_parameters([0.2, 0.4])
# will set RX(0.2) and RZ(0.4) and ignore RY

@scarrazza, @igres26 I used the name trainable for this flag following Tensorflow/Keras but I am open to alternative suggestions.

[codecov]

Codecov Report

Merging #294 (18a8d14) into master (568bb2d) will not change coverage.
The diff coverage is 100.00%.

@@            Coverage Diff            @@
##            master      #294   +/-   ##
=========================================
  Coverage   100.00%   100.00%           
=========================================
  Files           55        55           
  Lines        10583     10664   +81     
=========================================
+ Hits         10583     10664   +81     

Flag	Coverage Δ
unittests	100.00% <100.00%> (ø)

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Impacted Files	Coverage Δ
src/qibo/base/abstract_gates.py	100.00% <100.00%> (ø)
src/qibo/base/circuit.py	100.00% <100.00%> (ø)
src/qibo/base/gates.py	100.00% <100.00%> (ø)
src/qibo/tensorflow/cgates.py	100.00% <100.00%> (ø)
src/qibo/tensorflow/gates.py	100.00% <100.00%> (ø)
src/qibo/tests/test_parametrized.py	100.00% <100.00%> (ø)

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[scarrazza]

Looks good and follows our discussion. I have just one comment about get_parameters, in some context would be useful to have something like get_parameters(include_not_trainable=True) to retrieve all parameters for a later circuit reallocation, e.g. when you store the best model parameters and then reload from file.

[stavros11]

Looks good and follows our discussion. I have just one comment about get_parameters, in some context would be useful to have something like get_parameters(include_not_trainable=True) to retrieve all parameters for a later circuit reallocation, e.g. when you store the best model parameters and then reload from file.

Thanks for the review. I added this option to get_parameters.