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I read some paper about Quantum Circuit Optimization but I am on a low level. And have some experience in ML.

But what I don't understand is it possible that ML can help to optimize Quantum Circuits and how does that work ?

Maybe in QAOA case , what does it bring us to optimize the Circuits ?

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    $\begingroup$ Hi Jeff! I'd recommend you rephrase the question to reference papers you've seen / include more specificity in areas you're interested in. $\endgroup$
    – C. Kang
    Sep 14 '20 at 16:37
  • $\begingroup$ arxiv.org/pdf/1911.00789.pdf. // So what I am understand is that if you have more depth in your Circuits that the results are better but in a real Quantum Hardware you have the problem with the Noise .... so must do the "Error Mitigation" ..am I right ? :/ $\endgroup$
    – Jeff24
    Sep 15 '20 at 9:41
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Yes, some works have been in this area. Here is an example: "Quantum Circuit Learning" https://arxiv.org/abs/1803.00745

and also this paper, "Learning to learn with quantum neural networks via classical neural networks" https://arxiv.org/abs/1907.05415

and also this paper, "Experimental pairwise entanglement estimation for an N-qubit system :A machine learning approach for programming quantum hardware" https://arxiv.org/abs/1902.07754

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