论文标题
遍历量子计算机的时间序列分析
A walk through of time series analysis on quantum computers
论文作者
论文摘要
由于量子电路上的旋转组件，基于变异电路的某些量子神经网络可以被认为等于经典的傅立叶网络。此外，它们可用于预测连续函数的傅立叶系数。时间序列数据表示时间变量的状态。由于某些时间序列数据也可以视为连续功能，因此我们可以期望量子机学习模型能够在时间序列数据上成功执行许多数据分析任务。因此，重要的是研究用于时间数据处理的新量子逻辑并分析量子计算机上数据的内在关系。 在本文中，我们通过使用需要几个量子门的简单量子运算符，介绍经典数据预处理和对Arima模型进行预测的量子类似物。然后，我们讨论未来的方向以及一些可用于量子计算机时间数据分析的工具/算法。
Because of the rotational components on quantum circuits, some quantum neural networks based on variational circuits can be considered equivalent to the classical Fourier networks. In addition, they can be used to predict the Fourier coefficients of continuous functions. Time series data indicates a state of a variable in time. Since some time series data can be also considered as continuous functions, we can expect quantum machine learning models to do many data analysis tasks successfully on time series data. Therefore, it is important to investigate new quantum logics for temporal data processing and analyze intrinsic relationships of data on quantum computers. In this paper, we go through the quantum analogues of classical data preprocessing and forecasting with ARIMA models by using simple quantum operators requiring a few number of quantum gates. Then we discuss future directions and some of the tools/algorithms that can be used for temporal data analysis on quantum computers.
