Physics

This lecture discusses the stratification of regions in the space of real symmetric matrices. The points of these regions are Mandelstam matrices for momentum vectors in particle physics. The kinematic strata are indexed by signs and rank two matroids. Matroid strata of Lorentzian polynomials arise when all signs are nonnegative. We describe the posets of strata, for massless and massive particles, with and without momentum conservation. This is joint work with Veronica Calvo and Hadleigh Frost.

High school students learn how to express the solution of a quadratic equation in one unknown in terms of its three coefficients.  Why does this formula matter? We offer an answer in terms of discriminants and data. This lecture invites the audience to a journey towards non-linear algebra.

In this talk, I will survey recent developments about the connection between neural networks and models of quantum mechanics and quantum field theory. Previous work has shown that the neural network - Gaussian process correspondence can be interpreted as the statement that large-width neural networks share some properties with free, or weakly interacting, quantum field theories (QFTs). Here I will focus on 1d QFTs, or models of quantum mechanics, where one has greater theoretical control. For instance, under mild assumptions, one can prove that any model of a quantum particle admits a representation as a neural network. Cherished features of quantum mechanics, such as uncertainty relations, emerge from specific architectural choices that are made to satisfy the axioms of quantum theory. Based on 2504.05462 with Jim Halverson.

Helen Hargreaves, MSW, RSW will present a workshop for PI Residents, providing information on the basics of Emotion Theory, how to assess ones own needs and communicate them. This presentation will particularly focus on Autistic and other neurodivergent ways of experiencing emotions and stress and how to better support neurodivergent team members in the workplace. Helen Hargreaves is a Neurodivergent Therapist with over 15 years experience workings with Neurodivergent clients. She is the Director of Rainbow Brain, a social work group practice that focuses on providing queer, trans and neurodivergent affirming therapy. Please note that this will be a 1.5 hour session with presentation and experiential components.

We explore the interplay of quantum computing and machine learning to advance experimental protocols for observing measurement-induced phase transitions (MIPT) in quantum devices. In particular, we focus on trapped ion monitored circuits and apply the cross entropy benchmark recently introduced by [Li et al., Phys. Rev. Lett. 130, 220404 (2023)], which can mitigate the postselection problem. By doing so, we reduce the number of projective measurements -- the sample complexity required per random circuit realization, which is a critical limiting resource in real devices. Since these projective measurement outcomes form a classical probability distribution, they are suitable for learning with a standard machine learning generative model. In this work, we use a recurrent neural network (RNN) to learn a representation of the measurement record for a native trapped-ion MIPT, and show that using this generative model can substantially reduce the number of measurements required to accurately estimate the cross entropy. This illustrates the potential of combining quantum computing and machine learning to overcome practical challenges in realizing quantum experiments. 

High temperatures are typically thought to increase disorder. Here we examine this idea in Quantum Field Theory in 2+1 dimensions. For this sake we explore a novel class of tractable models, consisting of nearly-mean-field scalars interacting with critical scalars. We identify UV-complete, local, unitary models in this class and show that symmetry breaking $\mathbb{Z}_2 \to \emptyset$ occurs at any temperature in some regions of the phase diagram. This phenomenon, previously observed in models with fractional dimensions, or in the strict planar limits, or with non-local interactions, is now exhibited in a local, unitary 2+1 dimensional model with a finite number of fields.

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.