GBS_2.py

"""
The code presented in GBS_1.py is pretty deterministic and makes use of the derived results from the original
paper, so i can't really just immediately edit it to do the optimisation part itself, i will need to
derive a function that does it for me.
Inspiration for this can maybe come from:
https://journals.aps.org/pra/pdf/10.1103/PhysRevA.100.012326
https://github.com/XanaduAI/constrained-quantum-learning/blob/master/three_mode.py

The function of this file would be to investigate optimisation of different distance measures to see if improvement 
can be had? The code linked talking about constrained quantum learning is mainly aimed at optimising the fidelity
of the state being prepared, so there is a target state and the aim is to maximise the fidelity of the constructed state 
with the target state.
"""