论文标题
使用变分量子电路的一步时间序列预测
One-Step Time Series Forecasting Using Variational Quantum Circuits
论文作者
论文摘要
时间序列预测一直是机器学习领域中发人深省的话题。机器学习科学家将时间序列定义为一组在一致的时间步骤中记录的观察结果。而且，时间序列预测是分析数据并查找变量如何随时间变化的一种方式，从而预测未来价值。时间在此预测中非常重要，因为它显示了数据集对数据集和最终结果的坐标。它还需要一个大数据集来确定规律性和可靠性。量子计算机可能被证明是通过利用量子机械现象（如叠加和纠缠）来感知时间序列中趋势的更好选择。在这里，我们考虑使用变异量子电路的一步时间序列预测，并记录不同数据集的观察结果。
Time series forecasting has always been a thought-provoking topic in the field of machine learning. Machine learning scientists define a time series as a set of observations recorded over consistent time steps. And, time series forecasting is a way of analyzing the data and finding how variables change over time and hence, predicting the future value. Time is of great essence in this forecasting as it shows how the data coordinates over the dataset and the final result. It also requires a large dataset to ascertain the regularity and reliability. Quantum computers may prove to be a better option for perceiving the trends in the time series by exploiting quantum mechanical phenomena like superposition and entanglement. Here, we consider one-step time series forecasting using variational quantum circuits, and record observations for different datasets.
