金融中的量子机器学习：时间序列预测
Quantum Machine Learning in Finance: Time Series Forecasting
摘要：
我们探讨了新的使用参数化的量子线路（PQC）的量子神经网络（QNN）的预测时间序列信号与模拟量子前向传播的功效。时间信号由几个正弦分量（确定性信号）组成，与趋势和加性噪声混合在一起。PQC的性能与经典的双向长短期记忆（BiLSTM）神经网络进行比较。我们的研究结果表明，对于由小幅度噪声变化（高达确定性信号幅度的40%）组成的时间序列信号，只有几个参数的PQC与经典的BiLSTM网络相似，具有数千个参数，并且对于具有更高幅度噪声变化的信号，性能优于它们。因此，QNN可以有效地用于对时间序列进行建模，同时具有比量子计算机中的经典机器学习模型更快地训练的显著优势。
英文摘要：
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.
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