Optimizing measurement-based cooling by reinforcement learning

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Physical review

سال: 2022

ISSN: ['0556-2813', '1538-4497', '1089-490X']

DOI: https://doi.org/10.1103/physreva.106.033124