Enhancement of sensitivity in low-temperature quantum thermometry via reinforcement learning

We present a quantum thermometry method utilizing a probe-meter system and leveraging reinforcement learning (RL) to enhance sensitivity at low temperatures. Temperature information is exclusively obtained by coupling the probe qubit with the bath, facilitating efficient information extraction by measuring the meter system. Our evaluations, considering both dephasing and dissipative probe-bath couplings, demonstrate a significant enhancement in temperature sensitivity through meter-assisted thermometry, excluding the ultralow-temperature range. To further optimize performance, RL is employed to adjust both the interaction strength between the probe-meter system and the frequency of the meter system. Results showcase that RL effectively enhances temperature sensitivity across a broad low-temperature spectrum by improving average sensitivity within the specified temperature range. This optimization successfully expands the operational range of quantum thermometers. These findings highlight the promising potential of the RL approach for high-resolution, low-temperature quantum thermometry applications.