在科技飛速創新的時代，大數據分析被廣泛應用於生活的各方面。工業 4.0 為世界帶來了大數據分析的進步。近年來，量子計算也受到更多關注。人們注意到量子計算的計算速度潛力。因此，人們希望未來能夠將工業4.0和量子計算的優勢結合起來。在本論文中，我們專注於在量子計算中結合經典深度學習和量子深度學習算法來預測工業大數據問題。
經典神經網絡是進行預測和分類的最流行的數據挖掘技術。此外，人們在自然語言處理和強化學習中同樣會使用它。量子深度學習也用於基於量子比特的屬性進行二進制分類。我們試圖整合傳統深度學習和量子深度學習的特性來解決這些問題。因此，我們為預測問題設計了兩個混合量子卷積神經網絡模型。模型之間的區別在於 混合量子卷積神經網絡之前的量子比特的連接方式(Hybrid-QCNN)。本研究採用RMSE作為績效評價指標。我們使用 10 組數據集來測試兩種不同模型的預測準確性。結果顯示，兩種不同模型的預測精度和耗時​​有顯著差異，第一種混合量子卷積神經網絡比第二種混合量子卷積神經網絡好。

Big data analytics has been widely applied in every aspect of life for a period of time in the era of rapid technological innovation. Industry 4.0 has brought the advancement of big data analytics to the world. Quantum computing has also attracted more attention in recent years. People notice the potential for computational speed in quantum computing. Therefore, people want to be able to combine the advantages of industry 4.0 and quantum computing in the future. In this research, we focus on combining classical deep learning and quantum deep learning algorithms within quantum computing to predict industrial big data problems.
Classical neural network is the most popular data mining techniques to do prediction and classification. Moreover, people use it in natural language processing and reinforcement learning. Quantum deep learning is also used to do binary classification based on the properties of a qubit. We attempted to integrate the properties of traditional deep learning and quantum deep learning to solve the problems. Therefore, we designed two Hybrid-Quantum Convolution Neural Network (Hybrid-QCNN) models for prediction problems. The difference between the models is the connection method of the qubit before the Hybrid Classical–Quantum Convolutional Neural Network.
This study uses the RMSE as a performance evaluation index. We used 10 datasets to test the prediction accuracy of two different models. The results show that the prediction accuracy and consuming time of the two different models have significant differences. The first Hybrid-Quantum Convolution Neural Network is better than the second Hybrid-Quantum Convolution Neural Network in prediction accuracy and consuming time

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