By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made ...

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 ...

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 ...

This illustration draws a parallel between quantum state tomography and natural language modeling. In quantum tomography, structured measurements yield probability outcomes that are aggregated to ...

Collaboration between Europe and Japan in quantum technologies and high-performance computing (HPC) is taking a significant step forward with the launch of the Q-Neko project in 2026. The Q-Neko ...

Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization by rapidly predicting molecular interactions and properties. For instance, ...

Motivation: Our goal is to establish a local infrastructure and a group of colleagues and graduate students focusing on research in the Quantum-NLP and ML domain. We aim at preparing and running ...

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 an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

Orbital-free approach enables precise, stable, and physically meaningful calculation of molecular energies and electron densities By applying new methods of machine learning in quantum chemistry ...