  A digital quantum simulation for the extended Agassi model is proposed using
a quantum platform with eight trapped ions. The extended Agassi model is an
analytically solvable model including both short range pairing and long range
monopole-monopole interactions with applications in nuclear physics and in
other many-body systems. In addition, it owns a rich phase diagram with
different phases and the corresponding phase transition surfaces. The aim of
this work is twofold: on one hand, to propose a quantum simulation of the model
at the present limits of the trapped ions facilities and, on the other hand, to
show how to use a machine learning algorithm on top of the quantum simulation
to accurately determine the phase of the system. Concerning the quantum
simulation, this proposal is scalable with polynomial resources to larger
Agassi systems. Digital quantum simulations of nuclear physics models assisted
by machine learning may enable one to outperform the fastest classical
computers in determining fundamental aspects of nuclear matter.
