# How to use GPU to accelerate the hybrid QNN **URL:** \ **Category:** PennyLane Help\ **Created:** [March 2, 2024, 10:07pm UTC](https://discuss.pennylane.ai/t/how-to-use-gpu-to-accelerate-the-hybrid-qnn/4213 "2024-03-02T22:07:12Z")\ **Posts on this page:** 7\ **Page:** 1
**Author:** ![cheng](https://avatars.discourse-cdn.com/v4/letter/c/ecccb3/32.png) [@cheng](https://discuss.pennylane.ai/u/cheng)\ **Post date:** [March 2, 2024, 10:07pm UTC](https://discuss.pennylane.ai/t/how-to-use-gpu-to-accelerate-the-hybrid-qnn/4213/1 "2024-03-02T22:07:12Z")
Hi. I am tring to implement a quantum-classical hybrid neural network. But I found that the training is very slow. When I use the GPU, I meet some problem. Is there any way to use GPU for the training? ```auto import pennylane as qml import torch import torch.nn as nn import torch.nn.functional as F import os batch_size = 32 epochs = 30 lr = 1e-3 w_decay = 1e-4 os.environ["CUDA_VISIBLE_DEVICES"] = "0" device = torch.device('cuda') n_qubits = 16 dev = qml.device("default.qubit", wires=n_qubits) @qml.qnode(dev) def qnode(inputs, w): for i in range(n_qubits): qml.RX((inputs[0:batch_size, i]), wires=i) qml.Rot(*w, wires=0) for cont in range(15): qml.CNOT(wires=[cont, cont+1]) return (qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(qml.PauliZ(2)), qml.expval(qml.PauliZ(3)), qml.expval(qml.PauliZ(4)), qml.expval(qml.PauliZ(5)), qml.expval(qml.PauliZ(6)), qml.expval(qml.PauliZ(7)), qml.expval(qml.PauliZ(8)), qml.expval(qml.PauliZ(9)), qml.expval(qml.PauliZ(10)), qml.expval(qml.PauliZ(11)), qml.expval(qml.PauliZ(12)), qml.expval(qml.PauliZ(13)), qml.expval(qml.PauliZ(14)), qml.expval(qml.PauliZ(15))) weight_shapes = {"w": 3} class MNIST(nn.Module): def __init__ (self): super(). __init__ () # 1,28x28 self.pool = torch.nn.AvgPool2d(7, 7) # 4x4 self.qlayer = qml.qnn.TorchLayer(qnode, weight_shapes) self.fc1 = torch.nn.Linear(16, 10) def forward(self, x): bsz = x.shape[0] x = self.pool(x).view(bsz, 16) out = self.qlayer(x) out = self.fc1(out) out = F.log_softmax(out, dim=1) return out model = MNIST().to(device) a = torch.rand((1,1,28,28)).to(device) b = model(a) ``` But I get the error as follows. ```auto RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0, cpu! ``` Can anyone help me with this error? ---
**Author:** ![checcopo](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/checcopo/32/2337_2.png) [@checcopo](https://discuss.pennylane.ai/u/checcopo)\ **Post date:** [March 5, 2024, 9:31am UTC](https://discuss.pennylane.ai/t/how-to-use-gpu-to-accelerate-the-hybrid-qnn/4213/2 "2024-03-05T09:31:25Z")
I’m not an expert but it seems that you have activated the GPU of your pc, but the device called through PennyLane: ```auto dev = qml.device("default.qubit", wires=n_qubits) ``` does not support GPU, I mean probably you need to type some Torch interface inside it, otherwise I don’t know. ---
**Author:** ![cheng](https://avatars.discourse-cdn.com/v4/letter/c/ecccb3/32.png) [@cheng](https://discuss.pennylane.ai/u/cheng)\ **Post date:** [March 5, 2024, 2:06pm UTC](https://discuss.pennylane.ai/t/how-to-use-gpu-to-accelerate-the-hybrid-qnn/4213/3 "2024-03-05T14:06:19Z")
Thanks @checcopo. I have tried your suggestion. And it worked. However, the utilization rate of GPU is not high, and it seems that the training speed is not as good as that of CPU. This may be due to pennylane issues. Anyway thank you for your answer. 👍 ---
**Author:** ![checcopo](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/checcopo/32/2337_2.png) [@checcopo](https://discuss.pennylane.ai/u/checcopo)\ **Post date:** [March 5, 2024, 2:20pm UTC](https://discuss.pennylane.ai/t/how-to-use-gpu-to-accelerate-the-hybrid-qnn/4213/4 "2024-03-05T14:20:41Z")
Yeah, probably you are right. Anyway, I suggest you to have a look at PyTorch website to see which other little tricks you could apply to your code to save memory and stuffs like that. Also to check compatibility with your GPU and what PyTorch supports. Did you check the usage frequency of your GPU from the terminal? ---
**Author:** ![cheng](https://avatars.discourse-cdn.com/v4/letter/c/ecccb3/32.png) [@cheng](https://discuss.pennylane.ai/u/cheng)\ **Post date:** [March 5, 2024, 2:44pm UTC](https://discuss.pennylane.ai/t/how-to-use-gpu-to-accelerate-the-hybrid-qnn/4213/5 "2024-03-05T14:44:03Z")
Yeah. I checked the usage frequency of my GPU. It is just around 20%. I know there are a lot of factors that can change the usage frequency of a GPU. Hopefully I can figure out what’s going wrong. ---
**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 7, 2024, 6:54pm UTC](https://discuss.pennylane.ai/t/how-to-use-gpu-to-accelerate-the-hybrid-qnn/4213/6 "2024-03-07T18:54:19Z")
Hey @cheng! Using Torch’s GPU functionality is something we can’t guarantee a huge performance boost with. We have PennyLane-LightningGPU ([Lightning GPU device — Lightning 0.35.0 documentation](https://docs.pennylane.ai/projects/lightning/en/stable/lightning_gpu/device.html)), the downside there is that getting it to jive with Torch’s GPU handling isn’t going to work as you might expect (it’s all handled different under the hood). Let me know if that helps at all! ---
**Author:** ![cheng](https://avatars.discourse-cdn.com/v4/letter/c/ecccb3/32.png) [@cheng](https://discuss.pennylane.ai/u/cheng)\ **Post date:** [March 8, 2024, 1:52pm UTC](https://discuss.pennylane.ai/t/how-to-use-gpu-to-accelerate-the-hybrid-qnn/4213/7 "2024-03-08T13:52:19Z")
Thanks @isaacdevlugt I will try it.