Surface roughness is a crucial factor influencing the performance and
functionality of additive manufactured components. Accurate prediction of
surface roughness is vital for optimizing manufacturing processes and ensuring
the quality of the final product. Quantum computing has recently gained
attention as a potential solution for tackling complex problems and creating
precise predictive models. In this research paper, we conduct an in-depth
comparison of three quantum algorithms i.e. the Quantum Neural Network (QNN),
Quantum Forest (Q-Forest), and Variational Quantum Classifier (VQC) adapted for
regression for predicting surface roughness in additive manufactured specimens
for the first time. We assess the algorithms performance using Mean Squared
Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS) as
evaluation metrics. Our findings show that the Q-Forest algorithm surpasses the
other algorithms, achieving an MSE of 56.905, MAE of 7.479, and an EVS of
0.2957. In contrast, the QNN algorithm displays a higher MSE of 60.840 and MAE
of 7.671, coupled with a negative EVS of -0.444, indicating that it may not be
appropriate for predicting surface roughness in this application. The VQC
adapted for regression exhibits an MSE of 59.121, MAE of 7.597, and an EVS of
-0.0106, suggesting its performance is also inferior to the Q-Forest algorithm