# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 4 Experiment
### Experimental Analysis  
The experimental results show that the improved CNN network can also achieve good prediction results for trajectory data, a type of time series data. However, our QCNN-LSTM model achieved the highest accuracy, with the QCNN model slightly outperforming the CNN-LSTM model by about 5%. As the quantum encoding output of the quantum model changes the scale of the output data, RMSE and MAE are not compared. Instead, RMSE% and MAE% are used to make the results more accurate. Table 1 compares the results of the validation set, comparing the QCNN-LSTM model with the CNN-LSTM model and the CNN model with the QCNN model, validating that the addition of the quantum part improves model accuracy by an average of about 3%. Comparing the CNN-LSTM model with the CNN model validates that the addition of the LSTM part improves model accuracy by an average of about 6%. The QCNN-LSTM model achieves the best accuracy and RMSE%MAE% regardless of whether historical or discrete data is used as input, with accuracy above 98%. The QCNN-LSTM model with simulated  
time window input can achieve a maximum accuracy of 98.69%. The QCNN-LSTM model with discrete input is slightly lower but still has an accuracy of over 98%. Figure 6 shows the prediction performance of the CNN model, while Figure 7 shows the prediction performance of the CNN-LSTM model. The CNN-LSTM model appears to be more stable than the CNN model, but surprisingly, the CNN model performs better for out-of-sample predictions with time window input. The latter half of the model closely matches the original data. Combining Figures 6-9 shows that the quantum model in the figure can more accurately fit the trend of the original data, and the QCNN and QCNN-LSTM models can more accurately track the trend of longitude and latitude drift during the prediction process compared with the CNN and CNN-LSTM models. As indicated in the third section of the QCNN-LSTM model, quantum bits can represent multiple possible combinations of 1 and 0 simultaneously, while regular computer bits can only have two states of 0 or 1. A quantum computer with multiple superimposed quantum bits can cancel out a large number of possible calculation results simultaneously. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MAPE** & **ACCURACY\%** & **RMSE\%** & **MAE\%** & **TYPE** \\ \hline
0.017176 & 98.282445 & 1.938723 & 1.728665 & qcnn\_lstm\_discrete \\
0.013070 & 98.693044 & 1.517768 & 1.316671 & qcnn\_lstm\_time-window \\
0.183006 & 81.699374 & 16.895380 & 16.192391 & qcnn\_discrete \\
0.021535 & 97.846532 & 9.983868 & 6.016774 & qcnn\_time-window \\
0.074098 & 92.590254 & 10.180566 & 7.332311 & cnn\_lstm\_discrete \\
0.063243 & 93.675709 & 7.229650 & 6.256379 & cnn\_lstm\_time-window \\
0.114596 & 88.540435 & 11.830668 & 11.414632 & cnn\_discrete \\
0.086501 & 91.349870 & 9.383199 & 8.602466 & cnn\_time-window \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of running the four models. Figure 6: Performance of CNN model. All the models were designed with a single output during development; different predictions are required for predicting longitude and latitude. To make the visualization of results representative, the CNN model and CNN-LSTM predict latitude, QCNN predicts longitude, and the QCNN-LSTM model uses longitude for in-sample prediction and latitude for out-of-sample prediction. In terms of running time efficiency, the CNN model architecture runs faster than the QCNN model architecture. Without adding quantum structure, the average training time of the model is around 7 min, while the runtime of the QCNN model is about 12 min. The QCNN-LSTM model completes the run in about 17 min. Figure 8: Performance of QCNN model. Figure 7: Performance of CNN-LSTM mode. During the experiment, we identified the periodicity of ocean current data and determined its autocorrelation period to be 3 days during data preprocessing. On the other hand, AUV data is discrete and has no periodicity.