# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 5 Conclusions and Future Work  
The QCNN-LSTM model has the ability to effectively estimate multiple variables to some extent, even when these variables are not completely coupled or independent. However, the QCNN-LSTM model is sensitive to the degree of correlation between variables and the characteristics of the data. If there is a strong correlation or coupling between variables, the model can provide more accurate estimates. Conversely, if the correlation between variables is weak or if there are non-linear relationships, the model may face challenges and require more training and adjustments to capture their complex relationships. Furthermore, the design of the model and the quality of training also impact the estimation capability. Appropriate model architecture, proper selection of hyperparameters, and sufficient training data with effective training strategies are all important factors to ensure that the model can effectively estimate multiple variables. In summary, we employed common evaluation metrics for ocean trajectory prediction (or time series data) in our experiments to validate the proposed method. Based on the tabular data and graphical information, it can be concluded that our QCNN-LSTM model achieved an accuracy of over 98%, outperforming the comparison models for both discrete and periodically autocorrelated data. Furthermore, this high accuracy demonstrated stability across multiple subsequent experiments, and the model also achieved the lowest RMSE% and MAE%. These findings highlight the superiority of the model we utilized. However, this approach also has certain limitations. For handling long sequence data, I would recommend using the Transformer model. Long sequence data often involves longer dependencies, and the Transformer model, with its self-attention mechanism, effectively captures global dependencies and excels at modeling long-range dependencies. In contrast, the QCNN-LSTM model may face challenges with long-term memory retention. Additionally, when dealing with large datasets, the QCNN-LSTM model may have limitations, as quantizing the data and inputting it into QCNN-LSTM requires higher hardware requirements and computational power, potentially necessitating the use of quantum computers. The choice of model should consider specific task requirements, dataset size, and characteristics. In some cases, if the dataset is small or exhibits clear local patterns, the QCNN-LSTM model may still be a reasonable choice. This article presents the application of quantization in time-series data prediction, where we propose and develop the QCNN-LSTM model for sequence data prediction. Introducing quantization in the convolutional part of the model has improved computational power and avoids the problem of gradient explosion. Leveraging the unique properties of quantum bits to represent multiple possibilities simultaneously can lead to more complex calculations and higher accuracy. We have also improved the input method of the model  
Figure 11: Prediction Results of AUV Data. to allow for different input types, making the model more targeted and improving its generalization ability. As quantum hardware continues to improve, quantum models can be further optimized and scaled up to solve larger and more complex problems. Combining classical and quantum models can leverage both strengths to achieve higher accuracy and computational efficiency. Since the LSTM model is a popular time series prediction model, future work will also consider directly developing quantum LSTM models for multivariate regression. Furthermore, we plan to develop end-to-end prediction software that can efficiently perform predictions on different types of data. Regarding the adjustments and optimizations of the model structure, we will consider incorporating other types of neural network layers, such as the Attention Mechanism, to enhance the model's expressive power and generalization ability. LSTM models may encounter the issue of long-term dependencies when dealing with long sequences.