Research on crude oil futures price prediction methods: A perspective based on quantum deep learning

Crude oil is an important strategic resource, and it is necessary to predict its price change fluctuation accurately. To address this problem, the research combines QNN with DL model to construct a hybrid model. First, we combine the principle of quantum gate, quantum circuit, quantum superposition, and quantum entanglement to design the structure of the encoding layer, variable layer, and measurement layer. Second, we embed the designed QNN into DL models (RNN, LSTM, and GRU), optimize hidden layer parameters, and construct a QDL crude oil price prediction model. Next, this research uses the Shanghai crude oil futures price data and its influencing factors to verify the prediction effect of QDL model. The research result indicates that QDL model is better than the traditional DL model. The integrated QNN model can improve the accuracy of crude oil price prediction. Finally, we take QGRU as an example to change the QNN structure. We find that the R2 of QGRU model, which consists of the revolving gate Y, revolving gate Z, CNOT gate, and Pauli Z gate, can reach 0.9492. We verify the stability and feasibility of QDL model by changing the quantum parameters. Compared with the traditional GRU model, the best result of R2 is improved by 9.43 %. We further validated the predictive performance of the model under different dependent variables and influencing factors. Therefore, a reasonable QNN structure combination can improve the prediction accuracy, further providing a new idea for QDL models to optimize the crude oil futures market and risk management.