Canberra, Australia - Researchers at Australia's CSIRO have successfully applied quantum machine learning (QML) to semiconductor fabrication, marking a first in the field. This breakthrough, published in *Advanced Science*, demonstrates the practical use of quantum methods on real experimental data.
The team focused on modeling the Ohmic contact resistance of gallium nitride transistors. Accurate modeling is crucial for optimizing semiconductor design. They developed a Quantum Kernel-Aligned Regressor (QKAR) architecture.
The QKAR model outperformed seven classical machine learning algorithms. Dr. Muhammad Usman noted the QKAR technique's immediate applicability, requiring only five qubits. This suggests easy integration into existing processes, driving innovation in manufacturing.