
Hybrid Quantum-Classical Computing Accelerates Discovery of Light-Sensitive Materials
A research team has developed an innovative hybrid quantum-classical computing approach that combines quantum optimization, machine learning, and classical computations to efficiently screen thousands of diarylethene derivatives, successfully identifying five promising photochromic compounds with optimal properties for light-controlled drug delivery applications.