Universal compiling and (No-)Free-Lunch theorems for continuous variable quantum learning
Tyler Volkoff,
Zoë Holmes,
Andrew Sornborger
Abstract:Recent progress in quantum machine learning has shown that the number of input-output state pairs needed to learn an unknown unitary can be exponentially smaller when entanglement is used as a resource. Here, we study quantum machine learning in the context of continuous variable (CV) systems and demonstrate that similar entanglement-enhanced resource reductions are possible. In particular, we prove a range of No-Free-Lunch theorems, demonstrating a linear resource reduction for learning Gaussian unitaries usi… Show more
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