Quantum computing was in the spotlight in the latest Data Science Symposium lecture.
Stefan Bekiranov, an associate professor of biochemistry and molecular genetics at the University of Virginia, discussed his research in a presentation titled “Quantum Machine Learning for Biomedical Applications.”
The Data Science Symposium, hosted by UVA’s School of Data Science, is a recurring forum aimed at showcasing a wide variety of research topics in the field. Previous talks have covered diverse subjects, from the role of data science in baseball operations to the importance of evaluating medical tests.
At the outset of his presentation, Bekiranov explained his path to researching quantum computing -- an emerging technology that uses quantum mechanics to address challenges that are beyond the capabilities of classical computers to solve -- noting that he had been trained in quantum mechanics and had worked as a computational biologist for more than 20 years.
After publishing a piece on quantum computing with other researchers, they received a grant from the National Institutes of Health to develop a quantum algorithm.
In his presentation, Bekiranov explained that computing has a critical role to play in biological sciences but that issues of scale and complexity are increasingly presenting challenges. The question that he and his colleagues looked at was whether quantum computing algorithms could help yield important insights.
Bekiranov went on to detail his research and laid out his conclusions, including that there were ongoing challenges in inputting data into a quantum computer.
Still, he said, there was hope for a breakthrough, noting that he thought it was conceivable that "by the end of the decade, we've got something that could really begin doing things that are more powerful than a classical computer."