Recent Research Answers the Future of Quantum Machine Learning on COVID-19 – Analytics Insight

Posted: May 11, 2020 at 11:13 am

We have all seen movies or read books about an apocalyptic world where humankind is fighting against a deadly pathogen, and researchers are in a race against time to find a cure for the same. But COVID-19 is not a fictional chapter, it is real, and scientists all over the world are frantically looking for patterns in data by employing powerful supercomputers with the hopes of finding a speedier breakthrough in vaccine discovery for the COVID-19.

A team of researchers from Penn State University has recently unearthed a solution that has the potential to expedite the process of discovering a novel coronavirus treatment that is by employing an innovative hybrid branch of research known as quantum machine learning. Quantum Machine Learning is the latest field that combines both machine learning and quantum physics. The team is led by Swaroop Ghosh, Joseph R., and Janice M. Monkowski Career Development Assistant Professor of Electrical Engineering and Computer Science and Engineering.

In cases where a computer science-driven approach is implemented to identify a cure, most methodologies leverage machine learning to focus on screening different compounds one at a time to see if they can find a bond with the virus main protease, or protein. And the quantum machine learning method could yield quicker results and is more economical than any current methods used for drug discovery.

According to Prof. Ghosh, discovering any new drug that can cure a disease is like finding a needle in a haystack. Further, it is an incredibly expensive, laborious, and time-consuming solution. Using the current conventional pipeline for discovering new drugs can take between five and ten years from the concept stage to being released to the market and could cost billions in the process.

He further adds, High-performance computing such as supercomputers and artificial intelligence canhelp accelerate this process by screeningbillions of chemical compounds quicklyto findrelevant drugcandidates.

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

The funding from the Penn State Institute for Computational and Data Sciences, coordinated through the Penn State Huck Institutes of the Life Sciences as part of their rapid-response seed funding for research across the University to address COVID-19, is supporting this work.

Ghosh and his electrical engineering doctoral students Mahabubul Alam and Abdullah Ash Saki and computer science and engineering postgraduate students Junde Li and Ling Qiu have earlier worked on developing a toolset for solving particular types of problems known as combinatorial optimization problems, using quantum computing. Drug discovery too comes under a similar category. And hence their experience in this sector has made it possible for the researchers to explore in the search for a COVID-19 treatment while using the same toolset that they had already developed.

Ghosh considers the usage of Artificial intelligence fordrug discovery to be a very new area. The biggest challenge is finding an unknown solution to the problem by using technologies thatare still evolving that is, quantum computing and quantum machine learning.Weare excited about the prospects of quantum computing in addressinga current critical issue and contributing our bit in resolving this grave challenge. he elaborates.

Based on a report by McKinsey & Partner, the field of quantum computing technology is expected to have a global market value of US$1 trillion by 2035. This exciting scope of quantum machine learning can further boost the economic value while helping the healthcare industry in defeating the COVID-19.

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Recent Research Answers the Future of Quantum Machine Learning on COVID-19 - Analytics Insight

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