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Surface roughness is a crucial factor influencing the performance andfunctionality of additive manufactured components. Accurate prediction ofsurface roughness is vital for optimizing manufacturing processes and ensuringthe quality of the final product. Quantum computing has recently gainedattention as a potential solution for tackling complex problems and creatingprecise predictive models. In this research paper, we conduct an in-depthcomparison of three quantum algorithms i.e. the Quantum Neural Network (QNN),Quantum Forest (Q-Forest), and Variational Quantum Classifier (VQC) adapted forregression for predicting surface roughness in additive manufactured specimensfor the first time. We assess the algorithms performance using Mean SquaredError (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS) asevaluation metrics. Our findings show that the Q-Forest algorithm surpasses theother algorithms, achieving an MSE of 56.905, MAE of 7.479, and an EVS of0.2957. In contrast, the QNN algorithm displays a higher MSE of 60.840 and MAEof 7.671, coupled with a negative EVS of -0.444, indicating that it may not beappropriate for predicting surface roughness in this application. The VQCadapted 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.

Quantum Machine Learning Approach for the Prediction of Surface Roughness in Additive Manufactured Specimens