Researchers show that the quantum approximate optimization algorithm (QAOA) can achieve quantum speedups of a classically intractable problem

SZ3’s innovative approach to data compression wins IEEE best paper on big data award. Explore how it can transform scientific research.

Shadows and cutting may sound ominous, but they are being used to provide a more efficient method for the estimation of quantum observables.

Argonne researchers give future-focused flash talks on energy-efficient computing at critical Department of Energy workshop.

A team of researchers from Argonne National Laboratory has devised a new approach that significantly reduces the computational cost of x-ray ptychography, while maintaining high resolution.

In a recent article in Significance magazine, researchers addressed some of the factors affecting energy demands, with a particular focus on the role of statistics in energy forecasting.

Particle accelerators are vital tools for studying the structure and behavior of the particles that make up matter.

Computation has become an essential part of scientific research. Yet numerous questions remain about the role of the research software engineer (RSE) in that research.

The ever-increasing volume of scientific data necessitates new approaches to compress the data enough to fit simulation and experiment user constraints in terms of storage space, I/O speed, memory size.

Researchers from Argonne National Laboratory, the University of Chicago and the Riken Center for Computational Science, Japan, have now developed a tool that expresses code by abstractions that minimize code duplication.

As winter snowstorms, high winds and freezing temperatures caused myriad power outages throughout the United States in late December 2022, an obvious question was raised: Why can’t the energy grid handle such emergencies?

Argonne researchers collaborate on developing a new scheduler for smart city applications.

Argonne researchers develop new approach for cutting large quantum circuits into smaller subcircuits, enabling execution on smaller quantum devices.

Scientists develop multifidelity approach for reliability analysis of aerospace systems.

Cardinal, an open-source simulation software package for high-fidelity multiphysics solutions, was named a finalist for the 2022 R&D 100 awards.

Simulation has become a key part of scientific discovery – complementing experiment, helping guide engineering designs and providing insight into complex problems infeasible in the lab.

Wind farms are increasingly gaining attention as an alternative energy source. But the wakes generated by the wind turbines lead to energy losses, significantly degrading the efficiency of wind power plants.

Today’s computers are providing enormous power, enabling scientists to tackle complex problems previously considered intractable.

The final workshop report for the 2021 Randomized Algorithms for Scientific Computing (RASC) workshop has been released.

Argonne National Laboratory has a long and distinguished record of working with special functions.

Can one effectively upscale x-ray images from nanoimaging to macroscopic specimens within reasonable imaging times?

In the introduction to the July 2021 special issue of the journal SoftwareX, Keahey and her coeditors present a strong argument for giving software the respect it deserves – and the funding that must accompany it.

In a study published in a special issue of the journal Atmosphere, a team of researchers from Argonne National Laboratory presented a novel ground-based approach using machine learning for estimating cloud cover.

Kate Keahey, a senior computer scientist at Argonne National Laboratory and senior scientist at the University of Chicago Consortium for Advanced Science and Engineering, recently argued for a ​“silver lining” in the internet cloud environment.