Scientists in Australia have used quantum machine learning to model semiconductor properties more accurately, potentially transforming how microchips are designed and manufactured.
The hybrid technique combines AI with quantum computing to solve a long-standing challenge in chip production: predicting electrical resistance where metal meets semiconductor.
The Australian researchers developed a new algorithm, the Quantum Kernel-Aligned Regressor (QKAR), which uses quantum methods to detect complex patterns in small, noisy datasets, a common issue in semiconductor research.
By improving how engineers predict Ohmic contact resistance, the approach could lead to faster, more energy-efficient chips. It also offers real-world compatibility, meaning it can eventually run on existing quantum machines as the hardware matures.
The findings highlight the growing role of quantum AI in hardware design and suggest the method could be adopted in commercial chip production in the near future.
