Hi there,

I am trying to do a quantum ml task using the pennylane and PyTorch on a mac device with a quantum circuit that looks like the following:

```
@qml.qnode(dev, interface="torch", diff_method="parameter-shift")
def quantum_circuit(noise, weights):
weights = weights.reshape(depth, n_qubits)
# Initialise latent vectors
for i in range(n_qubits):
qml.RY(noise[i], wires=i)
# Repeated layer
for i in range(depth):
# Parameterised layer
for y in range(n_qubits):
qml.RY(weights[i][y], wires=y)
# Control Z gates
for y in range(n_qubits - 1):
qml.CZ(wires=[y, y + 1])
return qml.probs(wires=list(range(n_qubits)))
```

The datatype of the output of this quantum circuit when provided the input variables is ` Torch.float64`

During backpropagation I get the following error

```
File "/Users/aaronmkts/miniconda3/lib/python3.11/site-packages/pennylane/math/single_dispatch.py", line 526, in _asarray_torch
return torch.as_tensor(x, dtype=dtype, **kwargs)
TypeError: Cannot convert a MPS Tensor to float64 dtype as the MPS framework doesn't support float64. Please use float32 instead.
```

This error, as indicated seems to be an issue with the MPS framework. In order to fix this I need the computation/output of the circuit to use `Torch.float32`

as indicated, I cannot find how to make this the case in penny lane, the inputs (noise and weights) are both `Torch.float32`

datatypes. Any insight into how to adjust the output would be greatly appreciated. I tried taking the output of the circuit and casting it to `Torch.float32`

using `.to(torch.float32)`

however this doesn’t help as it still has to backprop along the part which is the wrong datatype.