Optimizing measurement-based cooling by reinforcement learning

Conditional cooling-by-measurement holds a significant advantage over its unconditional (nonselective) counterpart in the average-population-reduction rate. However, it has a clear weakness with respect to the limited success probability of finding the detector in the measured state. In this work, we propose an optimized architecture to cool down a target resonator, which is initialized as a thermal state, using an interpolation of conditional and unconditional measurement strategies. An optimal measurement-interval ruopt for unconditional measurement is analytically derived, which is inversely proportional to the collective dominant Rabi frequency Std as a function of the resonator's population in the end of the last round. A cooling algorithm under global optimization by reinforcement learning results in the maximum value for the cooperative cooling performance, an indicator to measure the comprehensive cooling efficiency for arbitrary cooling-by-measurement architecture. In particular, the average population of the target resonator under only 16 rounds of measurements can be reduced by four orders in magnitude with a success probability of about 30%.