The natural protein universe is vast, and yet, going beyond and designing new proteins not observed in nature can yield new ...

Understanding and predicting complex physical systems remain significant challenges in scientific research and engineering. Machine learning models, while powerful, often fail to follow the ...

In a world increasingly shaped by artificial intelligence, the question of how machines make decisions under uncertain ...

This study has proposed a detection framework, and implemented it using quantum machine learning (QML) approach by applying Support Vector Machine (SVM) enhanced with quantum annealing solvers.

Subspace learning and Support Vector Machine (SVM) are two critical techniques in pattern recognition, playing pivotal roles in feature extraction and classification. However, how to learn the optimal ...

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as ...

However, their support for problems with only binary variables severely restricts the scope of practical problems that can be efficiently embedded. We build analog iterative machine (AIM), the first ...