Colloquium

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 string theory.  Axions are expected to couple ultra-weakly with ordinary matter, which complicates their detection. However, thanks to recent theoretical, experimental, and observational progress these particles may be discovered or ruled out within the near future. I will review some of the recent ideas that may soon play a role in axion discovery, including computing the evolution of axions in the early universe using supercomputers and novel observational signatures of axions that emerge in extreme astrophysical environments such as supernovae.

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 state explores the accessible part of the system's Hilbert space.  I will illustrate how these measures separate integrable and chaotic quantum systems by considering examples including spin chains, quantum billiards, quantum many body systems, and Random Matrix Theory.  I will end by describing an application of these methods to a conjecture that geometrizes complexity in quantum gravity, and will comment on the relevance of these ideas for the break down of effective field theory near spacelike singularities.

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 of codes constitute distinct phases in a precise sense, and they furnish machine unlearnable/learnable phases of data.  I will demonstrate how conditional mutual information (CMI) serves as an essential quantity in characterizing the corresponding phases and transitions and thus provides a new diagnostic for both error correction thresholds and machine learnability of quantum and classical systems, including real-world data.  

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 and analytic framework for preparing quantum thermal states as a quantum analog of Markov chain Monte Carlo (MCMC) methods, and as a workable model of nature’s system-bath interaction.

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 problems. For approximating optimal polynomial fits over finite fields, DQI achieves a superpolynomial speedup over known classical algorithms. The speedup arises because the problem’s algebraic structure is reflected in the decoding problem, which can be solved efficiently. Whether DQI can also attain quantum advantage for algebraically unstructured optimization problems such as max-k-XORSAT remains an open question. One reason for optimism is that the sparsity of the clause structure in max-k-XORSAT is reflected in the dual decoding problem, which is for LDPC codes. I will describe some current lines of attack on this open question, as well as generalizations of DQI for preparing Gibbs states of Hamiltonians. This talk will target a broad audience without assuming deep background in quantum algorithms.

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 physics, developing powerful anomaly detection methods using generative AI techniques such as normalizing flows. These same methods, applied to astronomical data, have enabled discovery of stellar streams and ultra-faint dwarf galaxies. Generative methods also accelerate simulation-based inference across many domains, including gravitational wave detection at pulsar timing arrays. When combined with physics-informed neural networks, generative AI enables an entirely new model-free measurement of the dark matter density in the local Galactic neighborhood. Finally, I will discuss how generative AI in the form of agentic LLMs is creating new modes of human-AI collaboration that promise to further accelerate research. Taken together, these advances signal that we have entered a new and exciting era — one where the oldest questions about our universe may very well be answered with the help of the newest AI tools.

Coherent states conveniently represent the classically meaningful wavelike states of light, as opposed to the corpuscular number states, but superposing coherent states is mind-bogglingly unclassical, including as examples "cat states", "comb states" and "compass states". I present a history of superposing coherent states, especially for oscillators (including electromagnetic field states) and for spin, and extend to entangled coherent states. I then show that superposed coherent states are a viable path for photonic quantum computing and for reaching towards the ultimate limits of sensing. Finally, I discuss our experimental realisations (at University of New South Wales) of superposed coherent states in a spin-7/2 Antimony nucleus in a silicon substrate.

Symmetries are one of the most important concepts across many areas of theoretical physics. In this talk, I will explain how to think systematically and generally about the ways in which symmetries act in quantum many-body lattice systems, incorporating locality and the thermodynamic limit. One can think of this as a kind of many-body generalization of representation theory. In particular, this leads to a simple yet precise definition of the concept of an "anomaly" of a symmetry in lattice systems. I will discuss physical interpretations of these "lattice anomalies" and show how they are related to, but distinct from, anomalies in quantum field theory.

We are now in the era of multi-messenger astronomy, where neutron stars and black holes—the most extreme objects in the Universe—can be studied through both electromagnetic signals and gravitational waves. These compact remnants of massive stars provide unique windows into the short lives and deaths of their progenitors and the history of star formation across cosmic time. In this talk, I will show how we can apply cutting-edge computational models to uncover the origins of black holes and neutron stars and to address outstanding puzzles, including how and where the gravitational wave mergers detected by LIGO/Virgo/KAGRA are formed.