All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks

ArXi:2309.07250v2 Announce Type: replace-cross Variational algorithms require architectures that naturally constrain the optimization space to run efficiently. Geometric quantum machine learning achieves this goal by encoding group structure into parameterized quantum circuits to include the symmetries of a problem as an inductive bias. However, constructing such circuits is challenging as a concrete guiding principle has yet to emerge.