Substochastic Monte Carlo (SSMC) is a classical algorithm that utilizes time-varied substochastic birth-death chains to solve optimization problems. By slowly varying a chain with some known initial quasi-stationary distribution to one with an unknown QSD, SSMC can often remains close to the instantaneous QSD throughout the entire variation. By choosing a final chain which encodes some optimization problem, if this process succeeds it naturally produces the optimum. This technique is a classical analog of and was inspired by the quantum adiabatic optimization algorithm. 

SSMC was developed by Michael Jarret, Stephen Jordan, and Brad Lackey and supported in part by Booz Allen Hamilton, NIST, and QuICS.