Molecular Docking to Clean Up Pollution
Quantum simulation and quantum machine learning solution to accurately model the chemical process of molecular docking involved in removing organic pollutants in water/air.
OWNER
STATUS
Phase 1 – Outline
QUANTUM APPROACH
Machine Learning (Quantum Inspired)
SDGs
CONTRIBUTORS
Quandela
QunaSys
WHO
ORIRGIN OF CONTRIIBUTORS
CONTEXT/IMPACT:
More than 2 million new cases of colorectal cancer are diagnosed every year around the globe. The entire global population, living anywhere on earth, above the age of 45~50 can be potentially affected by this challenge. Colorectal cancer remained the second leading cause of cancer-related deaths, accounting for 12 to 14% of all cancers recorded in Europe in 2012, and contributes US$14 billion to annual healthcare costs in the United States alone. In Asia, incidence rates range from 49.3 in Japan, 24.7 in South Korea, and 35.1 in Singapore, and they are equally high in many African and South American countries, turning this into a global challenge.
HOW COULD QUANTUM HELP:
Conventional computing, implementing for instance Convolutional Neural Networks, transfer learning, and ensemble learning, struggle with the computational load of analyzing billions of hours of track images are used. By using quantum computing, it could be possible to solve some complex optimization and pattern recognition tasks more efficiently than classical computers. Quantum algorithms such as Quantum Neural Networks and Quantum Support Vector Machines could enhance the efficiency and accuracy of image analysis, and quantum features space could provide more nuanced insights from the data, potentially improving detection rates.