Neural networks techniques enable the efficient capture of electron correlation effects in reduced-dimensionality spaces.

In the search for rare physics events, extremely pure materials are essential. A partnership between PNNL and Ultramet has led to tungsten with low contamination from other elements.

Erik Lentz and Christian Boutan will research quantum information science-enabled discoveries in high energy physics with these awards.

Developing a new scheme to more efficiently solve quantum chemistry problems.

Researchers have created an algorithm that slashes the calculations necessary to prepare and customize data for quantum computing.

A potential quantum advantage for fluid dynamics simulations from molecular to atmospheric scales with a new formula for success.

CACTUS provides AI-powered cheminformatics for autonomous science.

Pacific Northwest National Laboratory researchers developed a new theoretical method for studying highly correlated systems.

Federal environmental permitting process to be fast-tracked with AI-powered tools and cloud-supported data analysis.

Nineteen PNNL-affiliated researchers have been named to Clarivate’s 2024 list of the world's most highly cited researchers.

The search for dark matter includes expertise in radio frequency signal detection, quantum sensing, and high-energy physics at PNNL.

ARQUIN computational pipeline provides a holistic framework for simulating a system with distributed quantum computing ingredients.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

Three PNNL researchers will receive five continuous years of funding for projects through highly competitive DOE Early Career Research Program awards.

Bylaska and team will aim to make computational design of functional materials more targeted, impactful, and user friendly through this award.

A new approach validates quantum algorithms for low-energy nuclear physics applications

A new quantum algorithm speeds up simulations of coupled oscillator dynamics.

As new paradigms in advanced computing take shape, computational chemistry researchers are finding new ways to solve challenging chemistry problems.

In a new approach, quantum flow algorithms are used to simulate many-body systems on quantum computers and effectively describe chemical processes.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.