Colloquium

The stunning capabilities of modern AI systems give rise to many questions regarding how they work and how much more capable they can possibly get. One way to gain additional insight is via synthetic models of data with tunable complexity, which can capture the basic relevant structures of real data…

Quantum computers promise to simulate quantum systems and answer open questions across science from condensed matter and quantum chemistry to particle physics and quantum gravity. Two problems stand in the way: how to build them, and how to use them. Each brings hard optimisation, inference, and…

Optimal transport (OT) theory, first conceived to solve problems of moving dirt, has since evolved into a powerful mathematical framework with far-reaching applications across machine learning, probability and statistics, and theoretical physics. In particle physics, OT underpins many modern machine…

Axions are some of the best-motivated beyond the Standard Model particle candidates at present. These ultralight particles may account for the cosmological dark matter and explain other outstanding problems in nature, such as the strong-CP problem; they also are now known to emerge generically in…

As little as five years ago, the image of 'AI for Math' focused on specialty models that could discover interesting examples or constructions, for example, graphs with interesting parameters, where our intuition might be lacking but computer checking is simple. With large language models this has…

I will describe new ideas relating quantum chaos to the complexity of time evolution. One approach treats physical time evolution as a quantum computation, and bounds the smallest quantum circuit that can simulate this evolution. The second approach quantifies how ergodically and rapidly a quantum…

Modern quantum experiments achieve coherences and scales once only dreamed of, pushing the limits of physics and computation. To understand and guide these advances, the questions we ask of quantum physics today---centered around the behavior of quantum information and complexity in large coherent…

Physics has been driven by the discovery of novel phases of matter, largely in materials. Recently, the advent of both quantum error correction and machine learning, viewed as physical phenomena, has compelled us to revisit the notion of a phase itself. I will show how decodable/undecodable regimes…

Since the 1980s, simulation of quantum many-body systems has been a leading candidate for practical quantum advantage. Yet computing thermal equilibrium properties, a central pillar of this vision, still lacks an end-to-end algorithmic solution. Today, I will present a general-purpose algorithmic…

Achieving superpolynomial speedups for optimization has long been a central goal for quantum algorithms. I will discuss Decoded Quantum Interferometry (DQI), a quantum algorithm descended from Regev's reduction, that uses the quantum Fourier transform to reduce optimization problems to decoding…

Generative AI — a class of algorithms that learn complex probability distributions from data — is opening new avenues for discovery across fundamental physics. In this talk, I will highlight several recent applications. At the LHC, we are ushering in a new paradigm of model-agnostic searches for new…