# Change output dtypes with Torch interface

**URL:** <https://discuss.pennylane.ai/t/change-output-dtypes-with-torch-interface/4354>\
**Category:** PennyLane Help\
**Created:** [March 26, 2024, 7:36pm UTC](https://discuss.pennylane.ai/t/change-output-dtypes-with-torch-interface/4354 "2024-03-26T19:36:57Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![Aaron\_Thomas](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/aaron_thomas/32/2291_2.png) [@Aaron\_Thomas](https://discuss.pennylane.ai/u/Aaron_Thomas)\
**Post date:** [March 26, 2024, 7:36pm UTC](https://discuss.pennylane.ai/t/change-output-dtypes-with-torch-interface/4354/1 "2024-03-26T19:36:57Z")

</div>

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:

```auto
@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

```auto
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.

---

<div class="post-metadata">

**Author:** ![isaacdevlugt](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/isaacdevlugt/32/1159_2.png) [@isaacdevlugt](https://discuss.pennylane.ai/u/isaacdevlugt)\
**Post date:** [March 28, 2024, 1:22pm UTC](https://discuss.pennylane.ai/t/change-output-dtypes-with-torch-interface/4354/2 "2024-03-28T13:22:56Z")

</div>

Hey @Aaron_Thomas,

I don’t have your full code, so I can’t try to replicate what’s going on. That said, I’d make sure that you’re using up-to-date versions of PL (currently v0.35.1) and torch (currently 2.2.2). I was able to see something similar on my end with v0.34.0 and 2.1.2 on my laptop.

Let me know if that helps!