yingmao / quantum-generative-adversarial-network Goto Github PK

Hi, I would like to know why is the probabilities calculated in such a manner:
def get_probabilities(circ,counts=1000): job = execute(circ, backend, shots=counts) results = job.result().get_counts(circ) try: prob = results['0']/(results['1']+results['0']) prob = (prob-0.5) if prob <= 0.005: prob = 0.005 else: prob = prob*2 except: prob = 1 return prob
Why is there a need to minus 0.5 from the measured probabilites and then multiply it by 2?

Why is the result I run is far from the paper？


Results in the paper:

I am currently trying to regenerate the results you got from the paper and I am stuck since some of the modules got deprecated, and I tried making changes according to the migration guide but currently the code is running but not generating any result in the sense that its getting stuck in one of the for loops and cannot even finish 1 epoch in hours which I believe should not take so much time.

Any help would be useful.
Thanks!

Dear Prof,
please can you kindly share the correct version of the code resulting in the same results in the paper?
I'm waiting for your reply.
Thanks a lot.

Dear Prof, please can you kindly share the code version for running this code on a real quantum computer not a simulator?