# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 1 Introduction  
Due to industrialization and population growth, global resources are becoming increasingly limited. As an essential resource for human survival in the 21 st century, the ocean will significantly contribute to human and social progress [1]. Predicting the trajectory of ocean-floating objects is essential in various fields, including maritime safety, environmental protection, search and rescue operations, and maritime transportation. The ability to accurately predict the trajectory of floating objects such as oil spills, marine debris, and drifting vessels is crucial for a timely and effective response to emergencies and events at sea. For instance, in the case of an oil spill, accurate trajectory prediction can help determine the potential environmental impact of the spill and plan and implement effective response measures. In search and rescue operations, trajectory prediction can help identify the possible location of missing vessels or individuals and allocate search resources more efficiently.  
The main problem in maritime trajectory prediction is forecasting the future trajectory of a vessel based on past observed vessel trajectory data. This is a challenging problem because the movement of vessels at sea is influenced by various factors, including ocean currents, wind speed, and the behavior patterns of the target vessel. Therefore, there is a need to develop an accurate method for predicting maritime vessel trajectories in order to provide reliable position and motion information at future time points.  
This paper proposes a quantum convolutional Long-Short-Term Memory network (QCNN-LSTM). We quantized the CNN part to improve the computational power ofthe model and more effectively solve the gradient explosion problem. The connected LSTM part effectively enhances the model's ability to predict sequence data and performs better spatiotemporal sequence prediction. The QCNN-LSTM method utilizes the efficient computation and strong fault tolerance advantages of QCNNs, which can better extract features and perform convolution operations on sequence data. Compared to traditional numerical methods, the QCNN-LSTM method can better capture the features of sequence data, improving the model's prediction accuracy and effectiveness. Compared with other similar structured neural network models, the QCNN-LSTM method can significantly reduce the training and prediction time of the model, thereby improving its practicality and reliability.  
The Section 1 of the paper introduces the background, problem statement, and significance of the research and outlines the research objectives and methods. The Section 2, related work, reviews and summarizes previous research findings and literature relevant to the research topic while also explaining the motivation for employing quantization techniques. The Section 3 presents the experimental part, providing a detailed description of the data used in the experiments and the evaluation criteria, and analyzing and discussing the results of the experiments. Finally, in Section 5, the paper is summarized and future work is discussed.