The technology described uses a nanomechanical platform and tiny cantilevers to detect multiple HIV antigens at high sensitivity in a matter of minutes. These silicon cantilevers are cheap and easy to mass produce and can be readily equipped with a digital readout. Built into a solar-powered device, this technology could be taken to hard-to-reach parts of the world where early detection remains a challenge to deliver fast interventions to vulnerable populations without waiting for a lab.

World leaders should look to existing international law on the use of force to address the threat of space becoming ever more militarized, a new study shows.

Kenneth Merz, PhD, of Cleveland Clinic's Center for Computational Life Sciences, and his team are testing quantum computing’s abilities in chemistry through integrating machine learning and quantum circuits.  

Chemistry is one of the areas where quantum computing shows the most potential because of the technology’s ability to predict an unlimited number of possible outcomes. To determine quantum computing's ability to perform complex chemical calculations, Dr. Merz and Hongni Jin, PhD, decided to test its ability to simulate proton affinity, a fundamental chemical process that is critical to life. 

Dr. Merz and Dr. Jin focused on using machine learning applications on quantum hardware. This is a critical advantage over other quantum research which relies on simulators to mimic a quantum computer’s abilities. In this study, published in the Journal of Chemical Theory and Computation, the team was able to demonstrate the capabilities of quantum machine learning by creating a model that was able to predict proton affinity more accurately than classical computing.