A New York technology company has won its first U.S. federal research contract to ...

The computational demands of today’s AI systems are starting to outpace what classical hardware can deliver. How can we fix this? One possible solution is quantum machine learning (QML). QML ...

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...

This illustration draws a parallel between quantum state tomography and natural language modeling. In quantum tomography, structured measurements yield probability outcomes that are aggregated to ...

The quantum tangent kernel method is a mathematical approach used to understand how fast and how well quantum neural networks can learn. A quantum neural network is a machine learning model that runs ...

The exponential growth of network complexity, traffic volume and security demands is challenging the scalability of classical automation frameworks. Artificial intelligence (AI) has improved network ...

Researchers cobbled together funding and time to show how quantum computing could aid in the development of drugs to help ...

Catalysts play an indispensable role in modern manufacturing. More than 80% of all manufactured products, from pharmaceuticals to plastics, rely on catalytic processes at some stage of production.

From superconductors and AI-driven quantum analysis to black hole physics, Day 2 of QMAT2026 highlighted cutting-edge ...

Quantum computing is quickly evolving from being a theoretical risk to a significant challenge for IT security.