Physical reservoir computing (RC) is a machine learning algorithm that
employs the dynamics of a physical system to forecast highly nonlinear and
chaotic phenomena. In this paper, we introduce a quantum RC system that employs
the dynamics of a probed atom in a cavity. The atom experiences coherent
driving at a particular rate, leading to a measurement-controlled quantum
evolution. The proposed quantum reservoir can make fast and reliable forecasts
using a small number of artificial neurons compared with the traditional RC
algorithm. We theoretically validate the operation of the reservoir,
demonstrating its potential to be used in error-tolerant applications, where
approximate computing approaches may be used to make feasible forecasts in
conditions of limited computational and energy resources.

我们引入了一种量子 RC 系统，利用共振腔中的受探测原子的动力学，提出的量子储层可以使用较少的人工神经元进行快速可靠的预测，理论上验证了储层的操作并展示了在计算和能源资源有限的条件下使用近似计算方法进行可行预测的潜力。