论文标题
用于古典和量子问题的量子储层计算实现
Quantum Reservoir Computing Implementations for Classical and Quantum Problems
论文作者
论文摘要
在本文中，我们采用了一个模型开放量子系统，该模型由两级原子系统组成，该系统耦合到Lorentzian光子腔，作为量子物理储层计算机的实例化。然后，我们将量子储层计算方法部署到图像识别的原型机器学习问题上。我们将量子物理储层计算机与使用相似体系结构的神经网络与量子物理储层计算机层的效果进行对比。值得注意的是，随着数据集大小的增加，量子物理储层计算机迅速开始执行常规神经网络。此外，量子物理储层计算机可在设定数据集大小的训练时期内具有出色的有效性，并在每个时期数字上都优于神经网络方法。最后，我们部署了量子物理储层计算机方法，以探索与开放量子系统的动力学相关的量子问题，在该量子系统中，原子系统合奏与与光子带隙材料相关的结构化光子储层相互作用。我们的结果表明，即使训练数据大小有限，量子物理储层计算机也同样有效地为量子问题产生有用的表示形式。
In this article we employ a model open quantum system consisting of two-level atomic systems coupled to Lorentzian photonic cavities, as an instantiation of a quantum physical reservoir computer. We then deployed the quantum reservoir computing approach to an archetypal machine learning problem of image recognition. We contrast the effectiveness of the quantum physical reservoir computer against a conventional approach using neural network of the similar architecture with the quantum physical reservoir computer layer removed. Remarkably, as the data set size is increased the quantum physical reservoir computer quickly starts out perform the conventional neural network. Furthermore, quantum physical reservoir computer provides superior effectiveness against number of training epochs at a set data set size and outperformed the neural network approach at every epoch number sampled. Finally, we have deployed the quantum physical reservoir computer approach to explore the quantum problem associated with the dynamics of open quantum systems in which an atomic system ensemble interacts with a structured photonic reservoir associated with a photonic band gap material. Our results demonstrate that the quantum physical reservoir computer is equally effective in generating useful representations for quantum problems, even with limited training data size.
