"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 ...

Researchers at the University of Tokyo have identified a precise sweet spot where quantum reservoir computing, a machine learning approach that treats quantum systems as computational engines, reaches ...

Reservoir computing is a promising machine learning-based approach for the analysis of data that changes over time, such as ...

By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...

This press release contains statements that may constitute "forward-looking statements." Forward-looking statements are subject to numerous conditions, many of which are beyond the control of ...

Integrating quantum computing into AI doesn’t require rebuilding neural networks from scratch. Instead, I’ve found the most effective approach is to introduce a small quantum block—essentially a ...

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 ...

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 ...

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 ...