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.

New apps help researchers with statistical analyses of data.

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.

A team of researchers from PNNL provided technical knowledge and support to test a suite of techniques that detect genetically modified bacteria, viruses, and cells.

PNNL’s software tool brings together artificial intelligence and satellite imagery to help responders assess damage and take action.

Newly developed ultra-low radiation cables reduce background noise for neutrino and dark matter detectors.

The award was given at the 29th International Conference on Information, Communication and Automation Technologies.

Recovery Simulator and Analysis tool further strengthens PNNL’s short-term resilience planning efforts for Puerto Rico.

The papers were featured in the security and quantum sessions at the 2023 International Symposium on Computer Architecture.

Researchers created the first open-source database of exotic materials eyed for use in quantum applications.

A combined experimental and theoretical study identified multiple interactions that affect the performance of redox-active metal oxides for potential electrochemical separation and quantum computing applications.

Research.com ranked PNNL joint appointee George Karniadakis as one of the top mathematicians in the world.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

A new approach broadens the reach of quantum computers to previously challenging systems.