Research on Time Series Prediction Model of Quantum Long Short Term Memory Network Fusion

This study proposes a novel hybrid prediction model (QGCN-LSTM) that combines quantum graph convolutional networks with classical LSTM. The model takes classical time series data as input and achieves classical quantum information conversion through a quantum encoding layer. Multi scale features are extracted through the collaborative computation of quantum graph convolutional modules (QGCN) and quantum gated loop units, and a quantum attention module is introduced to dynamically screen key information. Finally, the prediction results are generated through quantum measurement and classical output layer. In the time series prediction task of urban traffic flow, a benchmark model system covering classical, cutting-edge, and traditional architectures was constructed. The experimental results show that QGCN-LSTM utilizes quantum entanglement gates to establish non local road network associations, dynamically improves key node weights based on quantum state fidelity, and achieves deep compression of lines through quantum line pruning technology, effectively alleviating the common problem of "poor plateau" in quantum neural network training. In terms of prediction accuracy, the average absolute error (MAE) of its key hub nodes is reduced by 34.1% compared to the classical graph convolution LSTM (GCN-LSTM) model, and the spatial correlation index (SCI) is improved to 0.89. In addition, it also shows excellent performance in dynamic response, edge computing efficiency and other aspects, meeting the real-time requirements of traffic signal control system. This study provides an effective paradigm for the application of quantum classical collaborative architecture in complex spatiotemporal prediction tasks.