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'Estimator job failed' while using EstimatorQNN to train NN using TorchConnector #669
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Here is the working example and the error message:
error:
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I am facing the same issue. Can you please share if you found any workaround for it? |
No, I am still facing this issue. |
Hey! I faced a similar issue and thought it had something to do with the Torchconnector and my inputs not being formatted correctly. However, in my circuit, the inputs and weights were not bound properly to the circuit. Have a look into the number of parameters both your feature map and ansatz has and then map that back to the number of inputs and weights you are sending in. |
Hey, thank you for your reply! I checked the number of parameters (both feature map and ansatz params) and they match exactly with the number of input features and weights respectively. Is there something that I am missing? What did you mean when you said the inputs and weights were not bound properly to the circuit ? |
Hey did you find any solution?? |
I am facing this issue for past 3 days and it happens in sampler and estimator only one when i tried to integrate with noise model. In EstimatorQNN with esttimator parameter and in sampler with sampler parameters. |
i was facing the issue when i tried to integrate it with torchconnector |
I am facing the same problem using SamplerQNN and tried to debug. The problem happens in method Can this be fixed in future releases? In my opinion, when using EstimatorQNN or SamplerQNN with the option input_gradients=False, it should be able to calculate gradients with respect to the weights even if RawFeatureVector is used. Please see my example:
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I am using
EstimatorQNN
to train a NN usingTorchConnector
.This is how I define the QNN:
while training it using PyTorch, I am using the
NLLLoss
function. When I give aloss.backward()
command, it throws an error'Estimator job failed'
Can somebody help me with this?The text was updated successfully, but these errors were encountered: