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Main Authors: Mücke, Sascha, Finkeldey, Felix, Piatkowski, Nico, Siebrecht, Tobias, Wiederkehr, Petra
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2412.04048
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author Mücke, Sascha
Finkeldey, Felix
Piatkowski, Nico
Siebrecht, Tobias
Wiederkehr, Petra
author_facet Mücke, Sascha
Finkeldey, Felix
Piatkowski, Nico
Siebrecht, Tobias
Wiederkehr, Petra
contents In this article, we propose a novel quantum regression model by extending the Real-Part Quantum SVM. We apply our model to the problem of stability limit prediction in milling processes, a key component in high-precision manufacturing. To train our model, we use a custom data set acquired by an extensive series of milling experiments using different spindle speeds, enhanced with a custom feature map. We show that the resulting model predicts the stability limits observed in our physical setup accurately, demonstrating that quantum computing is capable of deploying ML models for real-world applications.
format Preprint
id arxiv_https___arxiv_org_abs_2412_04048
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Predicting Machining Stability with a Quantum Regression Model
Mücke, Sascha
Finkeldey, Felix
Piatkowski, Nico
Siebrecht, Tobias
Wiederkehr, Petra
Quantum Physics
In this article, we propose a novel quantum regression model by extending the Real-Part Quantum SVM. We apply our model to the problem of stability limit prediction in milling processes, a key component in high-precision manufacturing. To train our model, we use a custom data set acquired by an extensive series of milling experiments using different spindle speeds, enhanced with a custom feature map. We show that the resulting model predicts the stability limits observed in our physical setup accurately, demonstrating that quantum computing is capable of deploying ML models for real-world applications.
title Predicting Machining Stability with a Quantum Regression Model
topic Quantum Physics
url https://arxiv.org/abs/2412.04048