NSF Award
2211841
Many crucial artificial intelligence applications in health sciences do not have sufficient practical data. Even when one collects incredible amounts of data, the data has significant gaps because of the unique circumstances (health conditions) of each patient. Training sizeable artificial intelligence systems on such data often results in an output that is either unacceptable for making decisive medical pronouncements or the systems do not perform any better than conventional methodologies. As such, the project's goal is to develop artificial intelligence, particularly machine learning algorithms that train rapidly, minimize errors, and do not require significant human expertise. The project's novelty is utilizing emerging quantum computing (QC) algorithms that offer the potential for rapid training of models and the ability to find better solutions quickly. In this project, QC will be applied to machine learning to demonstrate the efficacy of QC-based methods in two challenging applications: (a) seizure detection on encephalography signals and (b) automatic interpretation of digital pathology images. Positively impacting the two high-level applications will allow automated systems to approach domain expert (human) performance and increase the impact of this technology in the medical field, which will impact countless humans worldwide. Access to the highest levels of QC research will create career development opportunities, encouraging high schoolers to pursue computer and information science and engineering careers. <br/><br/>In this project, adiabatic quantum annealing (QA) will be used to solve two significant computational challenges: (a) finding a global minimum and (b) sampling from complex probability distributions. It will be demonstrated that training that utilizes QA-supported sampling can find better parameters than conventional parameter optimization approaches, and it also overcomes the deficiencies of current machine learning algorithms in challenging applications, such as seizure detection on encephalography signals and automatic interpretation of digital pathology images. Through these developments, it is also expected of this project to demonstrate that a wide range of configuration spaces (undirected probabilistic graphical models trained with a variety of application-relevant data) that have the property of "difficult to find local valleys in the probability distribution" to be easily sampled with QA. These findings will be applied to deep generative models for superior classification and pattern reconstruction accuracy. The ability of QA to reach difficult to sample regions of the configuration space will benefit many machine learning applications.<br/><br/>This project is jointly funded by Foundations of Emerging Technologies (FET) and the Established Program to Stimulate Competitive Research (EPSCoR).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
