QSA-QConvLSTM: A Quantum Computing-Based Approach for Spatiotemporal Sequence Prediction

The ability to capture long-distance dependencies is critical for improving the prediction accuracy of spatiotemporal prediction models. Traditional ConvLSTM models face inherent limitations in this regard, along with the challenge of information decay, which negatively impacts prediction performance. To address these issues, this paper proposes a QSA-QConvLSTM model, which integrates quantum convolution circuits and quantum self-attention mechanisms. The quantum self-attention mechanism maps query, key, and value vectors using variational quantum circuits, effectively enhancing the ability to model long-distance dependencies in spatiotemporal data. Additionally, the use of quantum convolution circuits improves the extraction of spatial features. Experiments on the Moving MNIST dataset demonstrate the superiority of the QSA-QConvLSTM model over existing models, including ConvLSTM, TrajGRU, PredRNN, and PredRNN v2, with MSE and SSIM scores of 44.3 and 0.906, respectively. Ablation studies further verify the effectiveness and necessity of the quantum convolution circuits and quantum self-attention modules, providing an efficient and accurate approach to quantized modeling for spatiotemporal prediction tasks.