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 ...

Researchers have found a way to make the chip design and manufacturing process much easier — by tapping into a hybrid blend of artificial intelligence and quantum computing. When you purchase through ...

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 ...

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

A team of AI researchers at Google DeepMind, working with a team of quantum researchers at Google Quantum AI, announced the development of an AI-based decoder that identifies quantum computing errors.

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

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 ...

Large language models are powering a new generation of AI agents that could transform computational chemistry from a ...

The Graduate School of Information Science (GSIS) at Tohoku University, together with the Physics and Informatics (PHI) Lab at NTT Research, Inc., have jointly published a paper in the journal Quantum ...

Quantum Machines, a provider of advanced hybrid quantum-classical control solutions, announced today the release of Qualibrate (which the company spells QUAlibrate), an open-source framework for ...