Training deep quantum neural networks
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In this video Ramona Wolf gives an overview of our recently published paper “Training deep quantum neural networks” (see, e.g., https://www.nature.com/articles/s41467-020-14454-2 or https://arxiv.org/abs/1902.10445 ).
Here a natural quantum neural network architecture for fully quantum machine learning is proposed and an efficient training algorithm described.
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Good,
First of all thank you very much, this is very interesting and i would definitely read the paper.
I have a question about non linearity. The main strength of neural networks is the non linear term (the activations) which gives it enhanced abilities to model non linear functions. How is it inserted in QNN? and if it doesnt doesnt it have a big imapct? are there aditional effort of implementing it soon?
Thanks
Firstly, thank your for this explanation. I see this as an extremely powerful algorithm. However, I'm stuck with question. Could you explain me how the "k" matrix was derived at? You did mention about it, but I wasn't able comprehend it.
Really awesome presentation! Can you please share the slides?
First, thanx so much for this nice presentation and paper.
I have a question. In the classical training procedure the weights are updated.
In the quantum version, I do not see any weight or coupling between the qubits of different layers. Are the couplings hidden in Unitaries?
Because as far as I understand, the tool to be updated is the unitary operator. Then the unitaries should always be two-qubit operators?
Am I right?
An explicit comparison of QNN advantages over standard NN (if any) would have been VERY welcome.
Still Kudos for the presentation! 👍
Could you enable the automatic closed captions please?
do you love me?
Thanks for the talk 🙂
just the fact that you edited the video to have high quality slides is good, thumb up
Jesus guys are already training quantum neural networks. We are so screwed.
This is the future
11:58 start of QNNs