Organizers: Zohreh Davoudi (U. of Maryland), Ubirajara van Kolck (U. of Arizona and Université Paris-Saclay), Silas Beane (UW), and Maria Piarulli (Washington U.)

An important goal of modern nuclear physics is to accurately predict nuclear phenomena directly from quantum chromodynamics. One strategy to achieve this is to perform lattice QCD simulations of few-nucleon systems and match them to effective field theories, which in turn can serve as an input to “ab initio” many-body methods  for computing nuclear properties. Both lattice QCD and ab initio many-body methods generally require beyond-exascale classical computation, and are expected to be facilitated by machine learning and quantum computing techniques. Ideas in quantum information science , such as the role of entanglement in nuclear processes, can impact our understanding of nuclear phenomena. Such approaches to computation will prove essential in advancing this critical area of the nuclear-physics mission in the coming decade.