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 ...

Machine learning techniques that make use of tensor networks could manipulate data more efficiently and help open the black ...

Artificial intelligence has grown so large and power hungry that even cutting edge data centers strain to keep up, yet a technique borrowed from quantum physics is starting to carve these systems down ...

Learning how a physical system behaves usually means repeating measurements and using statistics to uncover patterns. That ...

Integrating quantum computing into AI doesn’t require rebuilding neural networks from scratch. Instead, I’ve found the most effective approach is to introduce a small quantum block—essentially a ...

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 ...

Quantum physics has a reputation for needing exotic hardware, from liquid-helium-cooled qubits to sprawling AI clusters, just to crunch through basic simulations. Now a new “physics shortcut” is ...

Silicon Quantum Computing ("SQC"), a leader in quantum computing and quantum machine learning, has achieved a breakthrough that positions the ...

There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, ...