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

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 ...

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 ...

Quantum computing has moved from physics labs into boardroom slide decks, promising to crack problems that leave even the ...

"Machine Learning in Quantum Sciences", outcome of a collaborative effort from world-leading experts, offers both an introduction to machine learning and deep neural networks, and an overview of their ...

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 ...

Quantum can learn from the AI hype cycle, finding ways to manage expectations of what could be a very transformative technology. In the near- and mid-term, we need to not overplay things and be ...

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 ...

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing. An experimental study shows that already ...