Learning Unitaries by Gradient Descent

31 Jan 2020Bobak Toussi KianiSeth LloydReevu Maity

We study the hardness of learning unitary transformations in $U(d)$ via gradient descent on time parameters of alternating operator sequences. We provide numerical evidence that, despite the non-convex nature of the loss landscape, gradient descent always converges to the target unitary when the sequence contains $d^2$ or more parameters... (read more)

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