I’m trying to optimise classical fisher information from a gaussian system simulated using
default.gaussian backend. I’m measuring the mean and variance and using that to generate the gaussian probability distribution classically which I then use to calculate the fisher information.
The problem arises when I try to optimise the circuit taking the Fisher Information matrix as the cost function. The
qml.GradientDescentOptimizer just returns zero as the cost and doesn’t update the parameters. It doesn’t give an error or anything suggesting that the objective function is not differentiable.
I also tried changing the inital values and learning rate.
What could be the problem here?
Thanks in advance