Catalina Albornoz

Results 11 issues of Catalina Albornoz

### Before submitting Please complete the following checklist when submitting a PR: - [ ] Ensure that your tutorial executes correctly, and conforms to the guidelines specified in the [README](../README.md)....

Add support for images on community demo cards.

enhancement

**Title:** Adaptive circuits for quantum chemistry **Summary:** **Relevant references:** **Possible Drawbacks:** **Related GitHub Issues:**

### Expected behavior I expect the code below to work with both lightning.qubit and default.qubit for any differentiation method. However qml.metric_tensor is failing for the cases outlined in the table...

bug :bug:

### Expected behavior I expect to be able to run QNGO with data and parameters. ### Actual behavior I get an error when I try to run this. ### Additional...

bug :bug:

### Feature details Remove the mention to `pennylane.optimize` in the documentation since it no longer exists. Eg remove it from [here](https://docs.pennylane.ai/en/stable/introduction/interfaces/numpy.html). ### Implementation _No response_ ### How important would you...

enhancement :sparkles:

The link to [Basic Aer provider documentation](https://qiskit.org/documentation/apidoc/providers_basicaer.html) [here](https://docs.pennylane.ai/projects/qiskit/en/latest/code/api/pennylane_qiskit.BasicAerDevice.html) is broken. I think Qiskit now wants people to use [BasicProvider](https://docs.quantum.ibm.com/api/qiskit/providers_basic_provider) instead.

### Expected behavior The [TorchLayer demo](https://pennylane.ai/qml/demos/tutorial_qnn_module_torch/) should work with diff_method='parameter-shift' ### Actual behavior It throws an error. ### Additional information This question originated from [Forum thread 4940](https://discuss.pennylane.ai/t/question-of-parameter-shift-method-in-broadcasting/4940). ### Source code...

bug :bug:

The [optimization section](https://pennylane.ai/qml/demos/tutorial_qubit_rotation/#optimization) of the qubit rotation demo is worded as if the demo was still using the default NumPy/Autograd interface. We need to update the wording to actually match...

good first issue

### Expected behavior You can do parameter broadcasting while having shadow_expval as the measurement. ### Actual behavior ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (100,3,3)->(100,3,3) (100,2,2)->(100,2,2)...

bug :bug: