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CSIRO Uses Quantum AI to Revolutionize Semiconductor Design

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.

Metamaterial Breaks Thermal Symmetry, Enables One-Way Heat Emission

Scientists have created a groundbreaking metamaterial that breaks Kirchhoff’s law by emitting 43% more mid-infrared radiation in one direction than it absorbs. Built from layered InGaAs and tested at 512°F with a 5T magnetic field, the structure exhibits record thermal nonreciprocity. This breakthrough enables controlled one-way heat flow, a step toward thermal diodes and transistors. Unlike previous weak and narrowband effects, this design works across multiple angles and wavelengths. The discovery has major implications for heat management, solar thermophotovoltaics, and energy harvesting technologies.