Mosquitoes have been transmitting the West Nile virus to humans in the United States for over 25 years, but we still don’t know precisely how the virus cycles through these pests and the other animals they bite. A federally funded project aims to help pin down the process by using mathematical models to analyze how factors like temperature, light pollution, and bird and mosquito abundance affect West Nile virus transmission. The ultimate goal is to advise health departments of the best time of year to kill the bugs.

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.  

A new large language model framework teaches LLMs to use an optimization solving algorithm to resolve complex, multistep planning tasks. With the LLMFP framework, someone can input a natural language description of their problem and receive a plan to reach their desired goal.