  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 classical constrained minimization. These quantum constraints
are generally obtainable via sampling and then machine learning on a large
number of systematically consistent quantum many-body states. We showcase our
perspective on 1D fermion chains and spin chains for applicability,
effectiveness, caveats, and unique advantages, especially for strongly
correlated systems, thermodynamic-limit systems, property designs, etc.
