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

TL;DR

This study compares three quantum algorithms for predicting surface roughness in additive manufacturing, finding that the Quantum Forest outperforms Quantum Neural Network and Variational Quantum Classifier in accuracy.

Contribution

First comprehensive comparison of quantum algorithms for surface roughness prediction in additive manufacturing, highlighting the superior performance of Q-Forest.

Findings

Q-Forest achieves lowest MSE and MAE among tested algorithms.

QNN and VQC show less accurate predictions with negative EVS.

Quantum algorithms can be effective tools for manufacturing quality prediction.

Abstract

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.