Forecasting Financial Risk using Quantum Neural Networks

A. E. Bouchti, Younes Tribis, Tarik Nahhal, C. Okar
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引用次数: 6

Abstract

There has been enormous attention in quantum algorithms for reinforcing machine learning (ML) algorithms. In the current paper, we present quantum neural networks (QNNs) and a method of training which is well in quantum system and is improved with momentum accession and parameter self adaptive algorithm, and we build a new financial risk forecasting model. We apply this model to the empirical research on the financial risk forecasting of some Moroccan companies. Then we will compare the findings with the standard artificial neural network (ANNs).
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利用量子神经网络预测金融风险
用于强化机器学习(ML)算法的量子算法受到了极大的关注。在本文中,我们提出了量子神经网络(QNNs)和一种在量子系统中很好的训练方法,并通过动量加入和参数自适应算法进行改进,建立了一种新的金融风险预测模型。本文运用该模型对摩洛哥部分企业的财务风险预测进行了实证研究。然后我们将结果与标准人工神经网络(ANNs)进行比较。
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