Quantum physics has a reputation for needing exotic hardware, from liquid-helium-cooled qubits to sprawling AI clusters, just ...

A team of physics educators from Italy, Hungary, Slovenia and Germany is focusing on a new approach to teaching quantum physics in schools. Traditional classroom teaching has tended to focus on ...

Reliably quantifying and characterizing the quantum states of various systems is highly advantageous for both quantum physics ...

New research on wormholes suggests that these theoretical shortcuts that connect distant points in the universe might be linked with the spooky phenomenon of quantum entanglement. As Charles Q. Choi ...

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.

Quantum machine learning is a hybrid approach that combines classical data with quantum computing methods. In classical computing, data is stored in bits encoded as a 0 or 1. Quantum computers use ...

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 ...

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: ...