Search Talks

Over the last half decade, the capabilities of large language models (LLMs) have leapt from preschooler to graduate student and beyond. This talk reviews recent progress in teaching LLMs to do science and reasoning, and speculates as to what it will mean for the future of theoretical physics if these trends continue.

For decades, we have known that most of the matter in the Universe is invisible. This unseen component—dark matter—accounts for roughly 85% of all matter and plays a central role in shaping cosmic structure. Yet, despite decades of experimental effort and remarkable ingenuity, its fundamental nature remains unknown. Dark matter provides compelling evidence for physics beyond the Standard Model, motivating a broad landscape of candidates ranging from weakly interacting massive particles to ultralight axions. Astrophysical observations offer a complementary avenue to laboratory experiments for testing dark matter models across a broad range of masses and interaction strengths. In this talk, I will present novel observational strategies designed to uncover signatures of new physics and highlight the opportunities opened by current and next-generation observatories. In particular, I will demonstrate how modern astrophysical observatories can be repurposed as laboratories for fundamental physics, using JWST infrared observations to search for axion dark matter and radio observations of cosmic filaments to test WIMP scenarios.

Deep generative models are emerging as powerful tools for solving physics problems, enabling new approaches to inference, sampling, anomaly detection, and signal reconstruction. In the first part of the talk, I will discuss how generative models, designed with physical principles such as symmetry and multi-scale structure, can be used to construct likelihood functions for cosmological inference at the field level, extracting 3–5× the information from weak gravitational lensing compared to traditional two-point statistics. This framework also enables anomaly detection of model misspecification and enhances interpretability through sample generation. I will also show that these physics-informed generative models apply beyond cosmology, including sampling lattice models in statistical physics and anomaly detection in collider physics. In the second part, I will discuss how generative models can be used to construct physically informed priors for inverse problems. This framework enables image reconstruction from intensity interferometry, opening a path to imaging supermassive black hole accretion disks. If time permits, I will also talk about how the same ideas apply to the gravitational wave background analysis from pulsar timing arrays, reconciling an apparent inconsistency with astrophysical predictions.

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. In recent work we have focused on sequences obtained from random walks on graphs, hypergraphs, and hierarchical graphical structures. I will present some recent empirical results regarding how transformers learn sequences arising from random walks on graphs. The focus will be on neural scaling laws, unexpected temperature-dependent effects, and sample complexity. If there is time, I will also discuss the effect of parameterization strategies on hyperparameter scaling laws, where we see the critical importance of appropriately scaling the embedding layer learning rate.

Axion-like particles can form clouds around rotating supermassive black holes through superradiant instability. Via axion-photon coupling, these clouds can imprint time-dependent oscillations on the observed electric vector position angle. I will present constraints on such clouds around M87* and Sgr A* using 2017 Event Horizon Telescope polarimetric data. The analysis is based on conjugate closure trace products, which are non-imaging and insensitive to dominant calibration systematics such as station gains and polarization leakage. I will highlight the contrast between the relatively stable M87* data and the highly variable Sgr A* case, and discuss the prospects of applying this pipeline to future EHT and more sensitive next-generation EHT observations.

The regularisation of entanglement entropy is integral to the possibility of it sourcing the Bekenstein-Hawking entropy. This regularisation ought to come from quantum gravity, which if covariant, can induce a regularisation scheme inapplicable directly on hypersurfaces, but instead suited to application over spacetime volumes. As such, the calculation of entanglement entropy should be similarly situated over spacetime volumes. I will explain how this can be done via a spectral equation using spacetime correlators, and briefly show an example calculation in causal set theory, a discrete theory of quantum gravity. There, a modified entanglement entropy scaling was obtained, due to the discreteness.

Modern physics is built upon three fundamental frameworks: quantum theory, general relativity, and information theory. In this talk, I will first introduce the field of relativistic quantum information, an operational framework that studies quantum information in relativistic settings by bringing together these three subjects. In particular, I will discuss the use of particle detectors—specifically, the Unruh–DeWitt model—to operationally define particles and extract resources from the quantum vacuum. Central to this talk is the protocol of magic harvesting, in which non-stabilizer resources essential for quantum advantage are extracted from the vacuum state of a quantum field. As an example, I will present our recent work showing that the amount of harvested magic, quantified by the mana, can serve as a probe of spacetime geometry, with particular emphasis on its dependence on the curvature and dimensionality of Anti-de Sitter space, as well as on the scaling dimensions of operators in conformal field theories.

Deformation quantization is a mathematical approach to understanding the quantization of classical mechanical systems. This talk will outline the procedure of deformation quantization and how it connects to the mathematical study of deformation theory. Time permitting, we will discuss some examples, connect these ideas to bulk-boundary systems, and explore higher dimensional analogues.

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 compilation problems — naturally suited to AI.  Along the first direction, I present a new paradigm for designing quantum error-correcting codes tailored to specific algorithms, yielding order-of-magnitude improvements on targeted tasks. Machine learning, paired with SAT and tensor-network environments, extends this approach beyond analytic and brute-force design. Along the second direction, I present an experimentally realised scheme for a universal fault-tolerant gate set, and show how machine learning optimises its key bottlenecks. Looking ahead, I discuss the open question of how to integrate large-scale quantum computers with AI to tackle more difficult problems.