Work as a function of protocol duration for the efficient erasure of an underdamped memory: isothermal to adiabatic transition

Abstract: We use evolutionary reinforcement learning to determine efficient time-dependent erasure protocols for an underdamped cantilever moving in a double-well potential, an experimental realization of a 1-bit memory. We investigate how the mean work $\langle W \rangle$ needed to erase a bit scales as a function of the protocol duration $τ$. We find two regimes, depending on how $τ$ compares to the relaxation time of the system $t_r$. For $τ\gg t_r$, the quasistatic isothermal regime, we recover Landauer's bound plus an overhead that scales as $1/τ$, similar to the overdamped case. By contrast, for $τ<t_r$ erasure becomes adiabatic and $\langle W \rangle$ grows more slowly than in the isothermal case. This growth is bounded from below as $1/τ$, which we derive using a gedanken optimal protocol. Finally, comparison with overdamped erasure shows that learned protocols can outperform protocols that are optimal subject to equilibrium boundary conditions.