Neural networks are one typical structure on which artificial intelligence can be based. The term ›neural‹ describes their learning ability, which to some extent mimics the functioning of neurons in our brains. To be able to work, several key ingredients are required: one of them is an activation function which introduces nonlinearity into the structure. A photonic activation function has important advantages for the implementation of optical neural networks based on light propagation. Researchers in the Stiller Research Group at the Max Planck Institute for the Science of Light (MPL) and Leibniz University Hannover (LUH) in collaboration with Dirk Englund at MIT have now experimentally shown an all-optically controlled activation function based on traveling sound waves. It is suitable for a wide range of optical neural network approaches and allows operation in the so-called synthetic frequency dimension.

Scholars at the School of Engineering of the Hong Kong University of Science and Technology (HKUST) have unveiled an innovation that brings artificial intelligence (AI) closer to quantum computing – both physically and technologically.

Researchers at the University of Utah and the University of California, Irvine (UCI), have discovered a newtype of spin–orbit torque. The study that published in Nature Nanotechnology on Jan. 15, 2025, demonstrates a new way to manipulate spin and magnetization through electrical currents, a phenomenon that they’ve dubbed the anomalous Hall torque.

Q.ANT, the leading startup for photonic computing, today announced the launch of its first commercial product – a photonics-based Native Processing Unit (NPU) built on the company’s compute architecture LENA – Light Empowered Native Arithmetics. The product is fully compatible with today’s existing computing ecosystem as it comes on the industry-standard PCI-Express. The Q.ANT NPU executes complex, non-linear mathematics natively using light instead of electrons, promising to deliver at least 30 times greater energy efficiency and significant computational speed improvements over traditional CMOS technology. Designed for compute-intensive applications such as AI Inference, machine learning, and physics simulation, the Q.ANT NPU has been proven to solve real-world challenges, including number recognition for deep neural network inference.

A recent study by researchers from CSIRO and the University of Melbourne has made progress in quantum machine learning, a field aimed at achieving quantum advantage to outperform classical machine learning. Their work demonstrates that quantum circuits for data encoding in quantum machine learning can be greatly simplified without compromising accuracy or robustness. This research was published Sept.12 in Intelligent Computing, a Science Partner Journal.

Researchers at the Institute for Quantum Electronics in Zurich have produced the core processing unit of a photonic neural network in which optical nonlinearity plays a key role in making the network more powerful.

A team of NUS researchers led by Associate Professor Lu Jiong from the Department of Chemistry and Institute for Functional Intelligent Materials, together with their international collaborators, have developed a novel concept of a chemist-intuited atomic robotic probe (CARP).

Quantinuum's AI team, led by Dr Stephen Clark, Head of AI at Quantinuum, has published a new paper applying these concepts to image recognition. They used their compositional quantum framework for cognition and AI to demonstrate how concepts like shape, color, size, and position can be learned by machines – including quantum computers.