Sparse power-of-two circuit connectivity provides task-dependent advantages for variational quantum learning, and a Monna-map qubit reordering enables short-range circuits to solve long-range problems.
This determines the set of unitaries that can be reached asymptotically, i.e., in the limit of sufficiently many layers and arbitrary parameters
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Variational Learning with Sparse Long-range Entangling Gates
Sparse power-of-two circuit connectivity provides task-dependent advantages for variational quantum learning, and a Monna-map qubit reordering enables short-range circuits to solve long-range problems.