Quantum Machine Learning – Overview, Opportunities and Challenges

The National Science Foundation in its 2023 Artificial Intelligence Research Institute Program Solicitation (NSF 23-610) highlights the potential benefit of developing AI methodologies which do not rely on “massive datasets” and the “continuing growth of data-driven models and their access to more data”.   New private sector companies such as Liquid AI are exploring dynamic machine learning methodologies to decrease training data set size and computer processing requirements and increase model interpretability.

The application of quantum computing to machine learning has the potential to contribute to addressing some of these current machine learning challenges of large data set size and computer processing requirements and also increase model flexibility and interpretability.

The talk provides an overview of quantum machine learning in light of current status of quantum computing and machine learning and develops application archetypes for the classification of the most promising quantum machine learning applications.  The talk reviews the development of quantum machine learning algorithms and explores several specific potential applications including in biology and chemistry.   The talk concludes with a discussion of some of the challenges of quantum machine learning and proposes possibilities for furthering the quantum machine learning research agenda.