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.

翻译：物理储层计算是一种利用物理系统动力学预测高度非线性和混沌现象的机器学习算法。本文提出了一种量子储层计算系统，该系统利用腔内探测原子的动力学特性。原子以特定速率经历相干驱动，产生受测量控制的量子演化。与传统储层计算算法相比，所提出的量子储层仅需少量人工神经元即可实现快速可靠的预测。我们从理论上验证了该储层的运行机制，证明了其在误差容忍应用中的潜力，在这些应用中，可在计算资源和能量资源有限的条件下采用近似计算方法实现可行的预测。