Presented by Aaron Lindenberg. As we reach the limits of high-speed computation based on silicon, ideas for the next generation of computers have focused on electrically switchable nanoscale devices that operate in ways similar to the neurons and synapses of...
If scaled up successfully, the team's new system could help answer questions about certain kinds of superconductors and other unusual states of matter.
When upgrades to the X-ray laser at the Department of Energy’s SLAC National Accelerator Laboratory are complete, the powerful new machine will capture up to 1 terabyte of data per second; that’s a data rate equivalent to streaming about one...
SLAC works with two small businesses to make its ACE3P software easier to use in supercomputer simulations for optimizing the shapes of accelerator structures.
Monika Schleier-Smith and Kent Irwin explain how their projects in quantum information science could help us better understand black holes and dark matter.
SLAC and Stanford researchers secure support for two projects that share one goal: to reduce the side effects of radiation therapy by vastly shrinking the length of a typical session.
Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.
Q-NEXT will tackle next-generation quantum science challenges through a public-private partnership, ensuring U.S. leadership in an economically crucial arena.
To find the best possible shape for an accelerator component (left), researchers often have to tweak a number of factors at the same time, which would be tedious and time-consuming if done by hand. Software like SLAC’s ACE3P allows them...
Particle accelerators are used every day in a wide range of scientific, medical and industrial applications. But did you know that the task of operating these machines is far from mundane? For example, for every experiment at SLAC’s X-ray laser...
