Machine learning has emerged as a powerful tool in condensed matter physics, offering new perspectives on the exploration of quantum many-body systems, phase transitions and exotic states of matter.

Artificial intelligence (AI) has become integral to our daily lives, from virtual assistants like Siri to personalized recommendations on Netflix. As AI technology advances, quantum machine learning ...

As artificial intelligence explodes in popularity, two of its pioneers have nabbed the 2024 Nobel Prize in physics. The prize surprised many, as these developments are typically associated with ...

Researchers explore quantum machine learning to detect financial risk faster in high-frequency trading, achieving promising accuracy in experimental models.

Researchers from Tel Aviv University have developed a new method for simulating complex quantum systems that can be combined with cutting edge AI techniques The density of 6 fermions in a 2D harmonic ...

STOCKHOLM — John Hopfield and Geoffrey Hinton were awarded the Nobel Prize in physics Tuesday for discoveries and inventions that formed the building blocks of machine learning. "This year's two Nobel ...

Within the STRUCTURES Cluster of Excellence, two research teams at the Interdisciplinary Center for Scientific Computing (IWR) have refined a computing process, long held to be unreliable, such 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 ...

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 ...

Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it? When Radha Mastandrea started her undergraduate physics program at MIT in 2015, she ...

Researchers employ machine learning to more accurately model the boundary layer wind field of tropical cyclones. Conventional approaches to storm forecasting involve large numerical simulations run on ...