July 26, 2024
The article titled “SoK: quantum computing methods for machine learning optimization” authored by Dr. Hamza Baniata (IoT-Cloud Group, Department of Software Engineering) has been accepted into Springer Nature’s Q1 journal Quantum Machine Intelligence.
The review paper analyzed state-of-the-art quantum computing methods used to solve the Hyperparameter optimization (HPO), neural architecture search (NAS) and quantum architecture search (QAS) problems. According to the paper, quantum computing (QC) has been proposed to address these challenges in more than 50 works since 2017. The paper provides a needed comprehensive review of such QC-based methods for solving HPO, NAS, and QAS, offering a qualitative and empirical analysis, taxonomy, and classification of existing works. Accordingly, it outlines promising future research directions and unresolved issues in this emerging field.
The paper can be read on Springer Link’s website.