Hi,

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

-Kannan