论文标题
用于斑点区势的量子储存计算
Quantum Reservoir Computing for Speckle-Disorder Potentials
论文作者
论文摘要
Quantum Reservoir Computing是一种机器学习方法，旨在利用具有内存信息的量子系统的动力学来处理过程。为了获得优势，它提出了从储层提供的量子资源以及简单快速训练策略结合使用的量子资源的可能性。在这项工作中，该技术是用量子储备引入的，并应用于找到附加量子系统的基态能量。使用线性模型训练量子储层计算机，以预测存在不同斑点势势的粒子的最低能量。分析了任务的性能，重点是从储层中提取的可观察到的数量，并且在采用两分点相关性时表明要增强。
Quantum reservoir computing is a machine-learning approach designed to exploit the dynamics of quantum systems with memory to process information. As an advantage, it presents the possibility to benefit from the quantum resources provided by the reservoir combined with a simple and fast training strategy. In this work, this technique is introduced with a quantum reservoir of spins and it is applied to find the ground-state energy of an additional quantum system. The quantum reservoir computer is trained with a linear model to predict the lowest energy of a particle in the presence of different speckle-disorder potentials. The performance of the task is analyzed with a focus on the observable quantities extracted from the reservoir and it shows to be enhanced when two-qubit correlations are employed.
