Issue when using batched data on StronglyEntanglingLayers template.[BUG]

[BenYyyyyy]

Expected behavior

I want to use the StronglyEntanglingLayers template to process batched data. the shape of the data is like [batch_size, layers, wires, 3].

Actual behavior

Some errors happened:

.../pennylane/templates/layers/strongly_entangling.py", line 209, in compute_decomposition
    weights[..., l, i, 0],
IndexError: index 10 is out of bounds for dimension 1 with size 10

Additional information

I check the strongly_entangling.py and find this bug may comes from this code:

201 line:  n_layers = qml.math.shape(weights)[0]

the correct code may be

201 line:  n_layers = qml.math.shape(weights)[-3]

Source code

No response

Tracebacks

No response

System information

I run pennylane on the server, and the bug I think is not related to the system.

Existing GitHub issues

• I have searched existing GitHub issues to make sure the issue does not already exist.

[trbromley]

Thanks @BenYyyyyy for posting! This does sound like a bug, we'll take a look and get back to you.

Would you be able to share the output of qml.about() or at least qml.version() to help us troubleshoot?

[BenYyyyyy]

Thanks @BenYyyyyy for posting! This does sound like a bug, we'll take a look and get back to you.

Would you be able to share the output of qml.about() or at least qml.version() to help us troubleshoot?

Platform info: Linux-5.14.21-150400.24.81_12.0.75-cray_shasta_c-x86_64-with-glibc2.31
Python version: 3.9.16
Numpy version: 1.23.5
Scipy version: 1.12.0
Installed devices:

• default.gaussian (PennyLane-0.30.0)
• default.mixed (PennyLane-0.30.0)
• default.qubit (PennyLane-0.30.0)
• default.qubit.autograd (PennyLane-0.30.0)
• default.qubit.jax (PennyLane-0.30.0)
• default.qubit.tf (PennyLane-0.30.0)
• default.qubit.torch (PennyLane-0.30.0)
• default.qutrit (PennyLane-0.30.0)
• null.qubit (PennyLane-0.30.0)
• lightning.qubit (PennyLane-Lightning-0.31.0)

[CatalinaAlbornoz]

Thanks for sharing this info @BenYyyyyy . I see that you're not using the latest version of PennyLane. Are you able to update it to v0.36 which is our current version? My colleague David is working on a fix for your bug but you'll need to update PennyLane in order to be able to use it.

You can run pip install pennylane --upgrade