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Simulation of Quantum Many-Body Systems on Amazon Cloud

by   Justin A. Reyes, et al.
University of Central Florida

Quantum many-body systems (QMBs) are some of the most challenging physical systems to simulate numerically. Methods involving tensor networks (TNs) have proven to be viable alternatives to algorithms such as quantum Monte Carlo or simulated annealing, but have been applicable only for systems of either small size or simple geometry due to the NP-hardness of TN contraction. In this paper, we present a heuristic improvement of TN contraction that reduces the computing time, the amount of memory, and the communication time. We demonstrate our heuristic with the Ising model on powerful memory optimized Amazon Web Services (AWS) x1.32x large EC2 instances, showing the viability of cloud computing for scientific applications.


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