  Quantum many-body systems (QMBs) are some of the most challenging physical
systems to simulate numerically. Methods involving approximations for tensor
network (TN) contractions have proven to be viable alternatives to algorithms
such as quantum Monte Carlo or simulated annealing. However, these methods are
cumbersome, difficult to implement, and often have significant limitations in
their accuracy and efficiency when considering systems in more than one
dimension. In this paper, we explore the exact computation of TN contractions
on two-dimensional geometries and present a heuristic improvement of TN
contraction that reduces the computing time, the amount of memory, and the
communication time. We run our algorithm for the Ising model using memory
optimized x1.32x large instances on Amazon Web Services (AWS) Elastic Compute
Cloud (EC2). Our results show that cloud computing is a viable alternative to
supercomputers for this class of scientific applications.
