Computational complexity

Is it possible to fill space “cubically” with shapes that act like spheres? A proof at the intersection of geometry and theoretical computer science says yes.

In Shang-Hua Teng’s work, theoretical and practical questions have long been intertwined. Now he’s turning his focus to the impractical.

Random circuit sampling, a popular technique for showing the power of quantum computers, doesn’t scale up if errors go unchecked.

Computer scientists this year learned how to transmit perfect secrets, why transformers seem so good at everything, and how to improve on decades-old algorithms (with a little help from AI).

A quantum approach to data analysis that relies on the study of shapes will likely remain an example of a quantum advantage — albeit for increasingly unlikely scenarios.

Zero-knowledge proofs allow researchers to prove their knowledge without divulging the knowledge itself.

Three computer scientists have posted a proof of the NLTS conjecture, showing that systems of entangled particles can remain difficult to analyze even away from extremes.

Why verify every line of a proof, when just a few checks will do?

Finding out whether a question is too difficult to ever solve efficiently depends on figuring out just how hard it is. Researchers have now shown how to do that for a major class of problems.