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Implementing and Generalizing the Quantum Natural Gradient optimizer. #21

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BryceFuller opened this issue Dec 11, 2019 · 7 comments
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@BryceFuller
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BryceFuller commented Dec 11, 2019

Abstract

An interesting optimizer has been proposed recently by Stokes et al which shows promise for use in VQE. Let’s implement it and test how well it works with stochastic noise!

image

This paper goes into a lot of detail about it.

This paper helps explain the approach more intuitively.

This blog post from Xanadu is also very useful. (it's also where I took the above image from)

Xanadu's pennylane platform has an implementation of this optimizer; however, pennylane is set up differently than qiskit-so it will not be a copy-paste situation.

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Deliverable

An optimizer module that we can use within qiskit. Some pretty graphs showing how well our optimizer performs under stochastic noise.

GitHub repo

https://github.com/oliverfunk/quantum-natural-gradient.git

@BryceFuller BryceFuller added the from Coach This is an idea from a coach label Dec 11, 2019
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@1ucian0 1ucian0 added the group ready The group is ready to start working label Dec 12, 2019
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1ucian0 commented Dec 12, 2019

mashaka molepo
Makakole Mafane
Simon Rosen
Oliver Funk
Rodney Osodo

coach: Bryce Fuller

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@BryceFuller BryceFuller changed the title Quantum data compression using quantum autoencoders. Implementing and Generalizing the Quantum Natural Gradient optimizer. Dec 13, 2019
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@qcamp qcamp added the group is full No more members are allowed in this group! label Dec 13, 2019
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