  Stimulated by the successful descriptions of strongly correlated electron
systems by fractionalized fermions, correspondence between interacting fermions
and non-interacting multi-component fermions is formulated in examples of the
Hubbard model. The formalism enables constructions of the neural network for a
quantum many-body solver represented by coupled noninteracting fermions. After
showing the exact correspondence of 1- and 2-site Hubbard model to
two-component noninteracting fermions, numerical algorithm of the quantum
machine learning for the Hubbard model is proposed. Benchmark for the 4-site
systems is successfully presented and promising future directions as well as
implications are discussed.
