  Correlated sampling has wide-ranging applications in Monte Carlo
calculations. When branching random walks are involved, as commonly found in
many algorithms in quantum physics and electronic structure, population control
is typically not applied with correlated sampling due to technical
difficulties. This hinders the stability and efficiency of correlated sampling.
In this work, we study schemes for allowing birth/death in correlated sampling
and propose an algorithm for population control. The algorithm can be realized
in several variants depending on the application. One variant is a static
method that creates a reference run and allows other correlated calculations to
be added a posteriori. Another optimizes the population control for a set of
correlated, concurrent runs dynamically. These approaches are tested in
different applications in quantum systems, including both the Hubbard model and
electronic structure calculations in real materials.
