"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 ...

A team of physics educators is focusing on a new approach to teaching quantum physics in schools. Traditional classroom teaching has tended to focus on presenting the history of the origins of quantum ...

A quiet transformation is unfolding in the way scientists design the materials that power modern technology. From faster ...

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 ...

Researchers in Sweden have developed a machine-learning approach that embeds the laws of physics directly into neural ...

Entanglement plays a crucial role in advancing quantum technologies and exploring quantum many-body simulations. Here, we introduce a protocol aided by neural networks for measuring entanglement in ...

Characterizing multipartite quantum systems is crucial for quantum computing and many-body physics. The problem, however, becomes challenging when the system size is large and the properties of ...

Atom interferometry, a technique that leverages the wavelike nature of atoms, has been pivotal in precision measurements, including satellite navigation and measuring the Earth's roundness.

What really happens in the quantum world? In this conversation, physicist Sean Carroll explores some of the deepest mysteries ...

Your phone finishes your sentences, your camera detects faces and your streaming app suggests songs you never thought you would want, thanks to classical AI systems. These are powerful logic engines: ...

Amid high expectations for quantum technology, a new paper in Science reports a proven quantum advantage. In an experiment, entangled light has allowed researchers to learn a system's noise with very ...