In a global first, researchers at Australia’s CSIRO used quantum machine learning to enhance semiconductor design, outperforming classical AI models. By modeling Ohmic resistance in GaN transistors, the team built a hybrid quantum–classical model using just 5 qubits. This Quantum Kernel-Aligned Regressor revealed subtle fabrication patterns that classical methods missed. Their model guided new device fabrication, resulting in higher-performing chips. This success proves quantum-enhanced design can generalize to real-world production, marking a major leap toward practical quantum advantage in materials science and electronics manufacturing.
