Quantum computing is a vast space that is rapidly evolving. Many of the advancements are technological - with a particular focus on quantum computing chips such as the recently announced Google Willow project. However, researchers are also developing more accessible cloud-based platforms, with the goal of getting closer to practical applications in industries such as materials science.
Research from Rosa Di Felice, professor of physics and astronomy and quantitative and computational biology at the USC Dornsife College of Letters, Arts and Sciences, and Naman Jain, a master's degree student at the USC Viterbi School of Engineering, on developing coating films that resist corrosion recently received recognition in the 2024 Airbus-BMW Quantum Computing Challenge. The award furthers advances the university's Frontiers of Computing "moonshot," a $1 billion-plus initiative led by USC President Carol Folt to support ethical research and development in quantum computing, artificial intelligence, robotics and other advanced computing fields.
USC News recently spoke with Di Felice about how she is applying quantum computing techniques to computational surface science.
You won the "smart coating" track of the 2024 Airbus-BMW Quantum Computing Challenge. What was your project's goal?
Di Felice: "Smart coating" is about the creation of coating films that protect the functional surfaces of materials from environmental and usage corrosion. In this case, the focus was materials used in the transport industry. The coats may be inorganic or organic. Organic coats are fabricated bottom-up from molecular components, which are appealing from the manufacturing viewpoint but have the drawback of toxicity for typical molecules that bind favorably to functional substrates.
My project demonstrated a multistep hybrid computational protocol that is scalable and will benefit from the developments of quantum technologies and quantum theory.
How may your research affect the manufacturing industry?
Di Felice: Ground-state energy computational prediction in materials science enhances the process of manufacturing. This type of computational prediction can screen a vast range of materials and configurations to avoid expensive experimental assessments. Quantum computing may enable a further step, by increasing the computational precision in crucial processes and consequently reducing experimental production costs.
What about theoretical applications?
Di Felice: One exciting area of theoretical application is drug design in medicine. This is a large multiscale problem that ranges from the chemical interaction of a drug with the target protein to the high-level prediction of docking configurations to the screening of potential drugs. The smallest-scale level, namely the drug-protein interaction, may involve the destruction and formation of bonds, which may benefit from a quantum mechanical computational treatment.
What's next for your research?
Di Felice: My research will continue to evolve in two distinct lines of thought, one in computational biology and the other in potential applications of quantum computing.
Currently, I'm working on designing a quantum computational protocol for electron transfer in biology, specifically in DNA replication. I'm also continuing my work on surface corrosion and will extend surface studies to include heterogeneous catalysis.
