用于预测增材制造试样表面粗糙度的量子机器学习方法
Quantum Machine Learning Approach for the Prediction of Surface Roughness in Additive Manufactured Specimens
AI总结：
本文首次比较QNN、Q-Forest和回归版VQC三种量子算法预测增材制造试样表面粗糙度，结果显示Q-Forest在MSE、MAE和EVS上综合表现最佳。
摘要：
表面粗糙度是影响增材制造构件性能和功能的关键因素。准确预测表面粗糙度对于优化制造工艺并确保最终产品质量至关重要。量子计算近来受到关注，被视为解决复杂问题和构建精确预测模型的一种潜在方案。在本研究论文中，我们首次对三种经改编用于回归的量子算法进行了深入比较，即 Quantum Neural Network（QNN，量子神经网络）、Quantum Forest（Q-Forest）和 Variational Quantum Classifier（VQC，变量子分类器），用于预测增材制造试样的表面粗糙度。我们使用均方误差（MSE）、平均绝对误差（MAE）和解释方差得分（EVS）作为评估指标来评估算法性能。研究结果表明，Q-Forest 算法优于其他算法，取得了56.905的MSE、7.479的MAE和0.2957的EVS。相比之下，QNN算法表现出更高的MSE，为60.840，MAE为7.671，并伴有-0.444的负EVS，表明其可能不适合预测该应用中的表面粗糙度。经改编用于回归的VQC表现出59.121的MSE、7.597的MAE和-0.0106的EVS，说明其性能同样逊于Q-Forest算法。
英文摘要：
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.
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