论文标题
类似量子电路的学习：一种快速可扩展的经典机器学习算法，其性能与量子电路学习相似
Quantum circuit-like learning: A fast and scalable classical machine-learning algorithm with similar performance to quantum circuit learning
论文作者
论文摘要
将近期量子设备应用于机器学习（ML）引起了很多关注。在这种尝试中，Mitarai等人。 （2018年）提出了一个框架，将量子电路用于监督的ML任务，这称为量子电路学习（QCL）。由于使用量子电路，QCL可以使用指数级的高维希尔伯特空间作为其特征空间。但是，与经典算法相比，其效率尚未探索。在这项研究中，使用称为Count Sketch的统计技术，我们提出了一种使用相同希尔伯特空间的经典ML算法。在数值模拟中，我们提出的算法在几个ML任务中显示出与QCL相似的性能。这提供了一个新的观点，可以考虑量子ML算法的计算和内存效率。
The application of near-term quantum devices to machine learning (ML) has attracted much attention. In one such attempt, Mitarai et al. (2018) proposed a framework to use a quantum circuit for supervised ML tasks, which is called quantum circuit learning (QCL). Due to the use of a quantum circuit, QCL can employ an exponentially high-dimensional Hilbert space as its feature space. However, its efficiency compared to classical algorithms remains unexplored. In this study, using a statistical technique called count sketch, we propose a classical ML algorithm that uses the same Hilbert space. In numerical simulations, our proposed algorithm demonstrates similar performance to QCL for several ML tasks. This provides a new perspective with which to consider the computational and memory efficiency of quantum ML algorithms.
