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...

Q-NEXT will tackle next-generation quantum science challenges through a public-private partnership, ensuring U.S. leadership in an economically crucial arena.

Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.

The newly launched Quantum Fundamentals, ARchitecture and Machines initiative will build upon existing strengths in theoretical and experimental quantum science and engineering at Stanford...

Monika Schleier-Smith and Kent Irwin explain how their projects in quantum information science could help us better understand black holes and dark matter.

Two projects will look for ways to link individual quantum devices into networks for quantum computing and ultrasensitive detectors.

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 leaders of SLAC's Technology Innovation Directorate discuss how their group supports the lab's most innovative projects.

SLAC works with two small businesses to make its ACE3P software easier to use in supercomputer simulations for optimizing the shapes of accelerator structures.

The Aqueous Battery Consortium of Stanford, SLAC, and 13 others seeks to overcome the limitations of a battery using water as its electrolyte.

Digital design engineer Abhilasha Dave’s passion for connecting machine learning and hardware is helping SLAC solve big data challenges.