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Ground-state properties via machine learning quantum constraints
Ground-state properties are central to our understanding of quantum many-body
systems. At first glance, it seems natural and essential to obtain the ground
state before analyzing its properties; however, its exponentially large Hilbert
space has made such studies costly, if not prohibitive, on sufficiently large
system sizes. Here, we propose an alternative strategy based upon the
expectation values of an ensemble of operators and the elusive yet vital
quantum constraints between them, where the search for ground-state properties
simply equates to simple, classical constrained minimization. These quantum
constraints are generally obtainable via machine learning on a large number of
sample quantum many-body states systematically consistent with physical
presumptions. We showcase our perspective on 1D fermion chains and spin chains
for applicability, effectiveness, and several unique advantages, especially for
strongly correlated systems, thermodynamic-limit systems, property designs,
etc.Comment: 6 pages, 4 figure
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