论文标题
量子储存器计算：近期量子设备上量子机学习的储层方法
Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices
论文作者
论文摘要
量子系统在粒子数量上具有指数级的自由度，因此提供了无法在常规计算机上模拟的丰富动力学。量子储层计算是一种在量子系统上使用如此复杂而丰富的动态的方法，例如时间机器学习。在本章中，我们解释了量子储层计算和相关方法，量子极限学习机和量子电路学习，从量子力学和机器学习的教学介绍开始。在最先进的量子设备上，所有这些量子机学习方法在实验上都是可行且有效的。
Quantum systems have an exponentially large degree of freedom in the number of particles and hence provide a rich dynamics that could not be simulated on conventional computers. Quantum reservoir computing is an approach to use such a complex and rich dynamics on the quantum systems as it is for temporal machine learning. In this chapter, we explain quantum reservoir computing and related approaches, quantum extreme learning machine and quantum circuit learning, starting from a pedagogical introduction to quantum mechanics and machine learning. All these quantum machine learning approaches are experimentally feasible and effective on the state-of-the-art quantum devices.
