Learning Unitaries by Gradient Descent

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 seq...

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Hauptverfasser:	Kiani, Bobak Toussi, Lloyd, Seth, Maity, Reevu
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Mathematics - Mathematical Physics Physics - Mathematical Physics Physics - Quantum Physics
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