Researchers at Australia’s CSIRO have achieved a world-first demonstration of quantum machine learning in semiconductor fabrication. The quantum-enhanced model outperformed conventional AI methods and could reshape how microchips are designed. The team focused on modeling a crucial—but hard to predict—property called “Ohmic contact” resistance, which measures how easily current flows where metal meets a semiconductor.

They analysed 159 experimental samples from advanced gallium nitride (GaN) transistors (known for high power/high-frequency performance). By combining a quantum processing layer with a final classical regression step, the model extracted subtle patterns that traditional approaches had missed.

Tackling a difficult design problem

According to the study, the CSIRO researchers first encoded many fabrication variables (like gas mixtures and annealing times) per device and used principal component analysis (PCA) to shrink 37 parameters down to the five most important ones. Professor Muhammad Usman – who led the study – explains they did this because “the quantum computers that we currently have very limited capabilities”.

Classical machine learning, by contrast, can struggle when data are scarce or relationships are nonlinear. By focusing on these key variables, the team made the problem manageable for today’s quantum hardware.

A quantum kernel approach

To model the data, the team built a custom Quantum Kernel-Aligned Regressor (QKAR) architecture. Each sample’s five key parameters were mapped into a five-qubit quantum state (using a Pauli-Z feature map), enabling a quantum kernel layer to capture complex correlations.

The output of this quantum layer was then fed into a standard learning algorithm that identified which manufacturing parameters mattered most. As Usman says, this combined quantum–classical model pinpoints which fabrication steps to tune for optimal device performance.

In tests, the QKAR model beat seven top classical algorithms on the same task. It required only five qubits, making it feasible on today’s quantum machines. CSIRO’s Dr. Zeheng Wang notes that the quantum method found patterns classical models might miss in high-dimensional, small-data problems.

To validate the approach, the team fabricated new GaN devices using the model’s guidance; these chips showed improved performance. This confirmed that the quantum-assisted design generalized beyond its training data.

Researchers have found that a metamaterial, a stack of InGaAs semiconductor layers, can emit significantly more mid-infrared radiation than it absorbs. When this sample was heated (~540 K) in a 5-tesla magnetic field, it exhibited a record nonreciprocity of 0.43 (about twice the previous best). In other words, it strongly violates Kirchhoff’s law and forces heat to flow one way. This demonstration of strong nonreciprocal thermal emission could enable devices like one-way thermal diodes and improve technologies like solar thermophotovoltaics and heat management.

According to the published study, the new device is made from five ultra-thin layers of a semiconductor called indium gallium arsenide, each 440 nanometers thick. The layers were gradually doped with more electrons as they went deeper and were placed on a silicon base. The researchers then heated the material to about 512°F and applied a strong magnetic field of 5 teslas. Under these conditions, the material emitted 43% more infrared light in one direction than it absorbed—a strong sign of nonreciprocity. This effect was about twice as strong as in earlier studies and worked across many angles and infrared wavelengths (13 to 23 microns).

By providing a one-way flow of heat, the metamaterial would serve as a thermal transistor or diode. It could enhance solar thermophotovoltaics by sending waste heat to energy-harvesting cells and aid in controlling heat in sensing and electronics. It has potential implications for energy harvesting, thermal control, and new heat devices

Challenging Thermal Symmetry

Kirchhoff’s law of thermal radiation (1860) states that at thermal equilibrium, a material’s emissivity equals its absorptivity at each wavelength and angle. Practically, this reciprocity means a surface that strongly emits infrared will absorb it equally well.

Breaking this symmetry requires violating time-reversal symmetry, such as by applying a magnetic field to a magneto-optical material. For example, a 2023 study showed that a single layer of indium arsenide (InAs) in a ~1 T magnetic field could produce nonreciprocal thermal emission. However, that effect was extremely weak and worked only at specific wavelengths and angles. Till now, magneto-optical designs have achieved only tiny emission–absorption imbalances under very restrictive conditions. The new achievement demonstrates that man-made materials can produce one-way thermal emitters.

The tropospheric mission of NASA was launched in 2023 to monitor pollution. It was abbreviated as TEMPO and has revolutionised the scientists’ observation of the air quality from space. It was located around 22,000 miles above the Earth, and it uses a spectrometer to collect daytime air quality data on an hourly basis over North America. It covers small areas within a few square miles and significantly advances technologies, offering only one-time readings per day. This mission was successful within 20 months at its prime phase from June 19, 2025, and is now extended till September 2026 because of the exceptional quality of the data.

TEMPO Tracks the Air Quality

As per NASA, TEMPO keeps a track of the pollutants such as nitrogen oxides, formaldehyde, and ozone in the troposphere, which is the lowest atmospheric layer. This layer gets triggered by the power plants, vehicle emissions, dust, smog, and wildfire smoke. It gives hourly data rather than once a day, said Laura Judd, a researcher at NASA. Through this, we get to know about the emissions change over time. Further, how to monitor smog in the city or wildfire smoke. Such a real-life incident helps astronomers understand the evolution of air pollution in detail.

The major milestone during this mission was to get sub-three-hour data, which allows quicker air quality alerts. This enhances the decision-making and helps the first responders, said the lead data scientist at NASA’s Atmospheric Science Data Centre, Hazem Mahmoud. With over 800 users, TEMPO has passed two petabytes of data downloads in a year. It proves the immense value of the health researchers and air quality forecasters.

NASA’s Collaboration with NOAA and SAO

NASA worked together with NOAA and the Smithsonian Astrophysical Observatory, the former producing the aerosol products for distinguishing smoke from dust and analysing the concentration. As per Xiong Liu, the principal investigator, these datasets enhance the forecast of pollution, improve the models, and support public alerts at the time of peak emissions.

NASA’s Earth Venture Instrument program is running the TEMPO mission and a global constellation of air monitors, along with GEMS of South Korea and Sentinel-4 of ESA. The formal mission review this and evaluate the progress, inform future space-based air quality efforts, and be helpful in refining the goals.