reduces depth of cnn so that it fits properly #1
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Introducing a batch size. In the paper, you claim a training advantage of Quantum NN over classical NN. In section 4.2 Fig 3b, we see that the classical neural network has a much larger loss than the quantum network. However, this is merely because you don't use a batch_size (i.e., you use the whole data in one batch). If you were to introduce a batch size, the classical net drastically outperforms all the others. Loss reaches ~0. The core of the issue is that you have a training issue in your classical net. (The reason is that your classical net is too deep for such a simple problem, which is why you run into this problem.)
fpetitzon
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In the paper, you claim a training advantage of Quantum NN over classical NN.
In section 4.2 Fig 3b, we see that the classical neural network has a much larger loss than the quantum network.
The other alternative is to use a classical net that is less deep, see this PR.
Here, loss is much better for the classical nnet.
The core of the issue is that you have a training issue in your classical net. (The reason is that your classical net is too deep for such a simple problem, which is why you run into this problem.)
I don't think the model you present in this paper supports your conclusion. In particular, in the conclusion you claim that "Overall, we have shown that quantum neural networks [...]to train faster [...]."