Korbinian Kottmann
Korbinian Kottmann
> @Qottmann, PauliVSpace was previously showing up in the docs but now it isn't. Is that intentional? I am still seeing it listed in `pauli` under _Classes_:  [qml.pauli doc](https://xanaduai-pennylane--5161.com.readthedocs.build/en/5161/code/qml_pauli.html)...
Now a full feature in v0.36 🙃
Hi @cnktysz good idea! As long as you can trace it back to a polynomial fit it should be relatively straight-forward. I.e. if you mean by exponential fit $y=c_0\exp(c_1 x)$...
Note that if you want to use mitiq extrapolators (without differentiability) this is already implemented! So you can just pass whichever extrapolation function from mitiq that you like in the...
Great! Let us know how it goes, happy to help! Related but off-topic: If your main concern is improving ZNE, another interesting direction is trying out different folding schemes (the...
Wait, is the default for finite shots finite_diff, not parameter shift? Either way, I think this isnt just a problem for finite diff though 😬 ``` import pennylane as qml...
> but for op=qml.prod(qml.I(0), qml.RX(theta, 1)) we have op.grad_method="F" (because we added op.grad_recipe=[none]*op.num_params in this PR). This makes param_shift fall back to finite_diff 😬 Oh 😅 How much of a...
Would have to do an appropriate re-packaging in https://github.com/PennyLaneAI/pennylane/blob/master/pennylane/ops/op_math/sum.py#L445 and https://github.com/PennyLaneAI/pennylane/blob/master/pennylane/ops/op_math/sum.py#L459 (same for `Prod`). I actually dont understand how to do that with `qml.math`, as I keep "losing" the `requires_grad`...
> ``` > qml.math.convert_like(qml.math.hstack(coeffs), coeffs[0]) > ``` Nice! This is what I was looking for! Thanks! @KetpuntoG you can use that in your code when you call `terms` to re-package...
>> We tried to verify this but unfortunately couldn't reproduce. > Ah 🤔 If I recall, it was @Qottmann who reported this. Definitely possible that I had some _other_ python...