Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries? Theoretical ...

Recent advances in machine learning have opened transformative avenues for investigating complex problems in string theory and geometry. By integrating sophisticated algorithms with theoretical ...

When a quantum computer processes data, it must translate it into understandable quantum data. Algorithms that carry out this 'quantum compilation' typically optimize one target at a time. However, a ...

From self-driving cars to facial recognition, modern life is growing more dependent on machine learning, a type of artificial intelligence (AI) that learns from datasets without explicit programming.

Quantum computing appears on track to help companies in three main areas: optimization, simulation and machine learning. The appeal of quantum machine learning lies in its potential to tackle problems ...

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: ...

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

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 ...

After decades of theory, quantum computing is moving toward real-world utility, with breakthroughs in error correction bringing both commercial opportunity and urgent cybersecurity risks closer to ...

Overview: Quantum AI combines quantum computing with artificial intelligence to solve complex problems beyond the reach of ...