Other

Rydberg atom arrays have emerged as powerful quantum simulators, capable of preparing strongly correlated phases of matter that are potentially challenging to access with classical computational methods. A major focus has been on realizing these arrays on frustrated geometries, aiming to stabilize exotic many-body states like spin liquids. In this talk, I will show how two-dimensional recurrent neural network (RNN) wave functions can be used to study the ground states of Rydberg atom arrays on the kagome lattice. For Hamiltonians previously investigated in this geometry, I will demonstrate that the RNN finds no evidence for exotic spin liquid phases or emergent glassiness. In particular, I will argue that signals of glassy behavior, such as a nonzero Edwards-Anderson order parameter seen in quantum Monte Carlo (QMC) studies, may arise from artifacts related to long autocorrelation times. These results highlight the potential of language model-inspired approaches, like RNNs, for advancing the study of frustrated quantum systems and Rydberg atom physics more broadly.

arXiv paper: https://arxiv.org/pdf/2405.20384

A review of asymptotic (more generally, boundary) symmetries will be given in the context of the Hamiltonian formulation. General features (such as the form of the symmetry generators and the structure of the algebra) as well as specific examples will be covered.  A particular attention will be paid to asymptotically flat spaces and the asymptotic BMS algebra, where nonlinear redefinitions will be shown to yield a supertranslation-invariant angular momentum.

Cosmology is undergoing a data revolution. Surveys such as the imminent Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory will deliver huge galaxy catalogues that provide critical tools for understanding the nature of dark matter and dark energy. However, in order to obtain accurate cosmological constraints from these enormous datasets, we need reliable ways of estimating galaxy properties using only photometry. I will present pop-cosmos: a forward modelling framework for photometric galaxy survey data, where galaxies are modelled as draws from a population prior distribution over redshift, mass, dust properties, metallicity, and star formation history. These properties are mapped to photometry using an emulator for stellar population synthesis, followed by the application of a learned model for a survey's noise properties. Application of selection cuts enables the generation of mock galaxy catalogues. This enables us to use simulation-based inference to solve the inverse problem of calibrating the population-level prior on a deep multiwavelength catalogue, COSMOS2020. We use a diffusion model as a flexible population-level prior, and optimise its parameters by minimising the Wasserstein distance between forward-simulated photometry and the real COSMOS2020 survey data. The resulting model can then be used to derive accurate redshift distributions for upcoming photometric surveys, to facilitate weak lensing and clustering science. I will show applications of this framework, demonstrating how we are able to extract redshift distributions, and make inferences about galaxy evolution. I will also discuss the use of pop-cosmos as a prior for performing inference on individual galaxies in a highly scaleable manner, as well as ongoing work to analyse data from the Kilo-Degree Survey (KiDS) in preparation for LSST.

I will introduce the Entanglement Bootstrap, a program to extract and understand the universal information characterizing a state of matter, starting from the local entanglement structure of a single representative state. This universal information is usually packaged in the form of a quantum field theory; the program therefore provides a surprising new perspective on quantum field theory. I will discuss what we can learn about gapped topological phases and their associated topological field theories, and about quantum critical points and their associated conformal field theories.

A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective talk, we face the increasingly loud elephant in the room and ask a question that has been hinted at by many but not explicitly addressed: Can the structure that allows one to avoid barren plateaus also be leveraged to efficiently simulate the loss classically? We present a case-by-case argument that commonly used models with provable absence of barren plateaus are also in a sense classically simulable, provided that one can collect some classical data from quantum devices during an initial data acquisition phase. This follows from the observation that barren plateaus result from a curse of dimensionality, and that current approaches for solving them end up encoding the problem into some small, classically simulable, subspaces. We end by discussing caveats in our arguments including the limitations of average case arguments, the role of smart initializations, models that fall outside our assumptions, the potential for provably superpolynomial advantages and the possibility that, once larger devices become available, parametrized quantum circuits could heuristically outperform our analytic expectations.

Astrophysical compact objects, neutron stars, and black holes are powerful sources of non-thermal electromagnetic emission spanning many orders of magnitude in photon energy, from radio waves to multi-TeV gamma rays. Despite multiple groundbreaking observational discoveries done in recent years, our understanding of the dynamics of relativistic plasmas that produce these emission signatures remains limited. In this talk, I will describe a few successful examples of modeling the observed light coming from these remarkable astrophysical laboratories using various numerical approaches. I will focus on advances in understanding coherent radio emission of rotating neutron stars, pulsars, and multi-wavelength flares from accreting black holes.