# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 2 Related Work
### Previous Research Results  
Traditional numerical methods are one of the most important means for predicting drift trajectories. [PERSON] et al. used the Lagrangian model to predict the drift trajectories in the South China Sea [2]. [PERSON] et al. studied the probabilistic prediction ability of the sea ice model NextSim used in the backtracking mode [3]. NeXtSIM is a continuous Lagrangian numerical model that uses viscoelastic rheology to simulate the response of ice to external forces. [PERSON] et al. established a backtracking model for source analysis of marine floating objects [4]. Based on the Monte Carlo-based integrated trajectory model, the Leeway model, they proposed using virtual spatio-temporal drift trajectories for real-source analysis. In standard ocean trajectory prediction, the prediction of oil spills [5; 6; 7; 8] and algal migration and diffusion belong to the particle prediction problem [9; 10]. In particle prediction, numerical methods and models are often used to simulate particle motion. However, due to the complexity and uncertainty of the marine environment, traditional numerical methods and models often face difficulties in accurately predicting the trajectory of floating objects. Compared with traditional numerical methods, neural network models have stronger adaptability and generalization ability, higher prediction accuracy and computational efficiency, and require less data. These advantages make neural network models have a wide range of application prospects in drift trajectory prediction and have become a hot topic and trend in current research. The problem of predicting ocean drift trajectories is essentially a sequence prediction problem. Currently, popular prediction models mainly include recurrent neural networks (RNNs) [11; 12], Long-Short-Term Memory Networks (LSTMs) [13; 14; 15; 16], Transformers [17; 18], and their variations. The use of recurrent neural networks (RNN) for ship trajectory prediction is also an extensive research field. [PERSON] et al. extended the deep learning framework of ship trajectory prediction by exploring how the recurrent encoder-decoder neural network predicts through Bayesian modeling. Some researchers use the sequence-to-sequence model based on deep learning, combined with historical AIS observation data and additional input information (such as ship intention), to predict the ship trajectory [19] and use the sequence-to-sequence model and spatial grid for trajectory prediction [20]. [PERSON] et al. proposed an efficient transformer-based model called Informer for long sequence time-series forecasting (LSTF) that addresses the issues of quadratic time complexity [21]. [PERSON] et al. used an improved transformer network, TrAISformer-A, to extract the correlation of AIS trajectories in the proposed enrichment space to predict the location of ships in the next few hours [22]. Convolutional neural networks (CNNs) can accurately extract features from multi-dimensional data and create highly accurate and robust models. However, they are not entirely suitable for learning time series and require additional processing. The Temporal Convolutional Network (TCN) uses one-dimensional convolutional layers to model sequences. These convolutional layers use a causal convolution operation that differs from traditional CNNs. TCN is a sequence modeling method based on convolutional neural networks (CNN) that can be used to process time series data. Compared with traditional recurrent neural networks (RNNs), TCNs can perform parallel computations, resulting in faster computation speeds. Additionally, TCN has good long-term dependency modeling capabilities [23; 24; 25]. There are clear differences in the use of TCN and CNN. TCN focuses on handling temporal data and modeling temporal relationships, making it suitable for temporal data analysis. It utilizes one-dimensional convolutional layers and temporal pooling layers to capture long-term dependencies and local features in temporal data.