论文标题
金融中的量子机学习：时间序列预测
Quantum Machine Learning in Finance: Time Series Forecasting
论文作者
论文摘要
我们探讨了新颖使用参数化量子电路（PQC）作为量子神经网络（QNN）的疗效，以预测带有模拟量子正向传播的预测时间序列信号。时间信号由几个正弦组件（确定性信号）组成，与趋势和添加噪声混合在一起。将PQC的性能与经典双向长期记忆（BILSTM）神经网络的性能进行了比较。我们的结果表明，对于时间序列信号，由小振幅噪声变化（确定性信号的振幅的40％）组成）PQC仅具有少数参数，类似于经典的Bilstm网络，具有成千上万的参数，并且具有较高振幅噪声变化的信号。因此，可以有效地将QNN用于模型时间序列，同时，与量子计算机中的经典机器学习模型相比，受过训练的显着优势。
We explore the efficacy of the novel use of parametrised quantum circuits (PQCs) as quantum neural networks (QNNs) for forecasting time series signals with simulated quantum forward propagation. The temporal signals consist of several sinusoidal components (deterministic signal), blended together with trends and additive noise. The performance of the PQCs is compared against that of classical bidirectional long short-term memory (BiLSTM) neural networks. Our results show that for time series signals consisting of small amplitude noise variations (up to 40 per cent of the amplitude of the deterministic signal) PQCs, with only a few parameters, perform similar to classical BiLSTM networks, with thousands of parameters, and outperform them for signals with higher amplitude noise variations. Thus, QNNs can be used effectively to model time series having, at the same time, the significant advantage of being trained significantly faster than a classical machine learning model in a quantum computer.
