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

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

The idea that quantum computing could transform medical artificial intelligence (AI) has gained momentum in recent years, driven by advances in cloud-accessible quantum platforms and hybrid computing ...

"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 ...

In this contributed article, data scientists from Sigmoid discuss quantum machine learning and provide an introduction to QGANs. Quantum GANs which use a quantum generator or discriminator or both is ...

Machine learning, and more generally, artificial intelligence, has achieved dramatic success over the past decade. This has been apparent in the tackling of notoriously challenging problems such as ...

IonQ today laid out its five-year roadmap for trapped ion quantum computers. The company plans to deploy rack-mounted modular quantum computers small enough to be networked together in a datacenter by ...

Telstra has completed a trial with Silicon Quantum Computing (SQC) that sought to apply quantum machine learning to boost network automation. The 12-month trial saw the pair leverage Watermelon, SQC’s ...

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 ...

Ever wonder what will happen when exabyte data stores are the norm, and even the parallelism of Hadoop can no longer provide the necessary processing power to address the data deluge? Quantum ...