Quantum ML may speed discovery of COVID-19 treatments

May 05, 2020 //By Rich Pell
Quantum machine learning may speed discovery of COVID-19 treatments
Researchers at Penn State say they are exploring the emerging field of quantum machine learning - which combines machine learning (ML) and quantum physics - to discover possible treatments for COVID-19.

Quantum machine learning, say the researchers, could be faster and more economical than the current methods used for drug discovery, which can take five to ten years from initial idea to market approval, and cost billions of dollars.

"Discovering any new drug that can cure a disease is like finding a needle in a haystack," says Swaroop Ghosh, the Joseph R. and Janice M. Monkowski Career Development Assistant Professor of Electrical Engineering and Computer Science and Engineering. "High-performance computing such as supercomputers and artificial intelligence can help accelerate this process by screening billions of chemical compounds quickly to find relevant drug candidates."

"This approach works when enough chemical compounds are available in the pipeline, but unfortunately this is not true for COVID-19," says Ghosh. "This project will explore quantum machine learning to unlock new capabilities in drug discovery by generating complex compounds quickly."

The researchers have previously worked on developing a toolset for solving particular types of problems, known as combinatorial optimization problems, using quantum computing. Drug discovery, say the researchers, falls into the same type of problem as those they previously worked on, which made it possible for them to pivot to searching for a COVID-19 treatment while using the same toolset that they had already developed.

"Artificial intelligence for drug discovery is a very new area," says Ghosh. "The biggest challenge is finding an unknown solution to the problem by using technologies that are still evolving - that is, quantum computing and quantum machine learning. We are excited about the prospects of quantum computing in addressing a current critical issue and contributing our bit in resolving this grave challenge."

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