Resetting Induces Memory Loss in Non-Markovian Processes

Abstract: Stochastic resetting is a powerful strategy known to accelerate the first-passage time statistics of stochastic processes. While its effects on Markovian systems are well understood, a general framework for non-Markovian dynamics is still lacking, mostly due to its mathematical complexity. Here, we present an analytical and numerical framework to study non-Markovian processes under resetting, focusing on the first-passage properties of escape kinetics from metastable states. We show that resetting disrupts the inherent time correlation, inducing Markovianity, thereby leading to an efficient escape mechanism. This work, therefore, provides a much needed theoretical approach for incorporating resetting into complex chemical and biological processes, which follow non-Markovian dynamics.