Stochastic resetting as a non-Markovian eraser in a quantum Otto heat engine

Abstract

We investigate the thermodynamic performance of a quantum Otto heat engine modulated by stochastic resetting, utilizing an exact discrete-time collision framework of a qubit coupled to a hierarchical non-Markovian environment. By evaluating the exact ensemble-averaged dynamics, we demonstrate that state-selective resetting functions as a targeted ‘non-Markovian eraser’ that repeatedly severs instantaneous system–memory correlations. Consequently, resetting dramatically dampens persistent, memory-induced heat-flux oscillations in strong-memory regimes, whereas its impact on effectively Markovian relaxation remains virtually negligible owing to the intrinsically weak environmental memory effects in this regime. By implementing an optimal stroke-dependent resetting protocol, the work extraction is monotonically maximized, displaying an approximately linear scaling in the effectively Markovian regime and a pronounced nonlinear boost under strong memory effects. Crucially, the effective efficiency remains close to the conventional Otto benchmark within numerical accuracy under effectively Markovian dynamics, whereas in the non-Markovian regime it increases monotonically with the resetting rate by actively erasing parasitic, memory-induced heat absorption. This work establishes state-selective resetting as a potent control strategy for managing environmental memory and optimizing nonequilibrium quantum thermal machines in complex open systems.