Challenges and opportunities in quantum machine learning for high-energy physics | Nature
Reviews Physics Skip to main content Thank you for visiting nature.com. You are using a …

Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …

Read the fine print | Nature Physics Skip to main content Thank you for visiting nature.com.
You are using a browser version with limited support for CSS. To obtain the best experience …

Sampling from known probability distributions is a ubiquitous task in computational science,
underlying calculations in domains from linguistics to biology and physics. Generative …

Quantum computing has been proven to excel in factorization issues and unordered search
problems due to its capability of quantum parallelism. This unique feature allows …

Quantum physics has changed the way we understand our environment, and one of its
branches, quantum mechanics, has demonstrated accurate and consistent theoretical …

Chemical and physical properties of complex materials emerge from the collective motions
of the constituent atoms. These motions are in turn determined by a variety of interatomic …

In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

By mid-2019, both Peter and myself had found ourselves numerous times in situations
where we were asked to define what Quantum Machine Learning is (and what it isn't), or …

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of
hybrid quantum-classical models for classical or quantum data. This framework offers high …