Volume 4 Issue 12, December 2022

Artificial intelligence may uncover new scientific concepts that defy human intuition, but will researchers be able to understand and operate with them? This scenario might seem like science fiction, but physicists have faced it before.

In 2002, an experiment with ultracold atoms emulated a textbook condensed-matter physics phenomenon: the phase transition from a superfluid to a Mott insulator. Two decades later, Immanuel Bloch and Markus Greiner ponder how far quantum simulation with ultracold atoms has come.

As artificial intelligence (AI) makes increasingly impressive contributions to science, scientists increasingly want to understand how AI reaches its conclusions. Matthew D. Schwartz discusses what it means to understand AI and whether such a goal is achievable — or even needed.

Amanda J. Carr provides tips on using X-rays to probe interface ions.

Sarah Malik explains how quantum random walks can be used to model the cascades of quarks and gluons resulting from the proton–proton collisions at the Large Hadron Collider.

Non-Hermitian theory consists of mathematical structures that are used to describe open systems, which can give rise to non-Hermitian topology not found in Hermitian systems. This Review provides an overview of non-Hermitian band topology and discusses recent developments, such as the non-Hermitian skin effect and non-Hermitian topological classifications.

Scientific understanding is one of the main aims of science. This Perspective discusses how advanced computational systems, and artificial intelligence in particular, can contribute to driving scientific understanding.

For many complex or living systems, it is impossible to individually sample all their units, but subsampling can heavily bias the inference about their collective properties. This Perspective presents the subsampling problem and reviews recent developments to overcome this fundamental limitation.