Hi,
One can run ExpValCost() and then access the produced samples using device._samples.

In order to compute a gradient, qml optimizers perform 2 evaluations.
For example, in the case of 1-D parameter shift rule, we evaluate the circuit for theta + s and theta - s where theta is a scalar parameter and s is a scalar offset.

I would like to store the samples that were generated for theta + s and theta - s. How can I do this?

I imagine it should be something like

evaluate the function for theta + s
samples_1 = device._samples
evaluate the function for theta - s
samples_2 = device._samples

However, I donâ€™t want to compute samples again, I want to reuse samples that resulted from the gradient computation.

If I understand it correctly, you wish to run an optimization using the parameter-shift rule, and get the intermediate samples generated by it at the same time. Is that correct?

Unfortunately, I believe that might be difficult without changing the implementation of it since all the calculations and processing of the the samples happen internally, and arenâ€™t stored anywhere. The only solution I can think of would be to implement your own parameter-shift gradient, store the samples generated for each shift, and then supply the gradient function to the optimizers as a custom gradient via the grad_fn keyword in the optimizerâ€™s step method. I will double-check with some of my colleagues to see if there might be another work-around for this.

Let me know if you have any more questions regarding this (or any other issues or thoughts you might have).

Hi @Einar_Gabbassov! Yes, as @theodor mentioned, this is currently not possible using the built-in gradient logic in PennyLane. However, this is a useful feature to add, and something we will take into account while improving the gradient rules.

In the meantime, I think your main option would be to hand-code in the gradient rule. For example, you could have a function that accepts a QNode, and returns both parameter-shift samples and the gradient (the mean of the samples).