Abstract: I'll give theorems characterizing finite-dimensional quantum theory's framework of density matrices (states) and POVM elements (measurement outcomes) for describing systems by simple postulates whose physical and informational meaning and appeal is clear. Each theorem first characterizes a class of Euclidean Jordan-algebraic (EJA) systems. This is already only a slightly larger class than the usual quantum theory: real, complex, and quaternionic quantum theory, systems whose state spaces are balls, and 3-dimensional octonionic quantum theory. Complex quantum theory then follows from "local tomography", or "energy observability": the generators of continuous symmetries of the state space (potential reversible dynamics) are observables.
The first characterization (with Cozmin Ududec and John van de Wetering) uses: (1) Homogeneity: any strictly positive element of the cone of unnormalized states may be taken to any other by a symmetry of this cone, (2) Pure Transitivity: any pure state may be taken to any other pure state by a symmetry of the normalized state space
Time permitting, I'll discuss a second characterization (with Joachim Hilgert), using: (1) Spectrality: every state is a convex combination of perfectly distinguishable pure states, (2) Strong Symmetry: every set of perfectly distinguishable pure states may be taken to any other such set (of the same size) by a symmetry of the state space.
The physical, informational, and operational significance of the postulates will be discussed.
*We strongly encourage attendees to use their full name (and if possible, their UMD credentials) to join the zoom session.* https://umd.zoom.us/j/94291869736?pwd=phbbyGHG3wNcUD3gFoNtA97uYyAyKX.1
Abstract: Operator learning is the task of approximating mappings between Banach spaces towards emulation of expensive computer models or simulation of complex physical systems. In this talk I will discuss a general mathematical framework for this task based on simple kernel regression techniques and show numerical benchmarks that highlight its effectiveness and accuracy in comparison to neural net techniques. Afterwards I will talk about a different approach to operator learning via equation discovery that enables the simulation of physical processes in very scarce data regimes.
Abstract: One creates a fibered 3-manifold by thickening a surface by the interval and gluing its ends by a surface homeomorphism. In the finite-type setting, much is known about how the topological data of the gluing homeomorphism determine geometric information about the hyperbolic 3-manifold. Currently, there is a lot of research activity surrounding end-periodic homeomorphisms of infinite-type surfaces.
As an "infinite type" analogue to work of Minsky in the finite-type setting, we show that given a subsurface Y of S, the subsurface projections between the "positive" and "negative" Handel-Miller laminations provide bounds for the geodesic length of the boundary of Y as it resides in the end-periodic mapping torus.
In this talk, we'll discuss the motivating theory for finite-type surfaces and closed fibered hyperbolic 3-manifolds, show these techniques may be used in the infinite-type setting, and how our main theorems return results to the closed, fibered setting.
Abstract: Non-consumptive effects, such as fear of depredation, can strongly influence predator-prey dynamics. There are several ecological and social motivations for these effects in competitive systems as well. In this work, we consider the classic two species ODE and PDE Lotka-Volterra competition models, where one of the competitors is "fearful" of the other. We find that the presence of fear can have several interesting dynamical effects on the classical competitive scenarios. Notably, we show novel bi-stability dynamics for fear levels in certain regimes. Furthermore, the effects of several spatially heterogeneous fear functions are investigated in the spatially explicit setting. In particular, we show that a weak competition-type situation can change to competitive exclusion under certain integral restrictions on the fear function. Applications of these results to ecological as well as sociopolitical settings are discussed, which connect to the "landscape of fear" (LOF) concept in ecology. Using the test case of northern spotted and barred owl populations in the Pacific Northwest region of the United States, we evaluate if this fear (co-occurrence) model can generate more robust population estimates than previous models. We then evaluate if potential co-occurrence effects among Barred and Northern Spotted Owls are uni- or bi-directional. Lastly, we leverage the best-performing model to evaluate the degree to which a recently proposed barred owl culling program may help recover Northern Spotted Owl populations.
Abstract: Alice and Bob are playing a guessing game. A room contains infinitely many boxes, labeled with the positive integers. Each box contains a real number. Alice will go into the room, open some but not all boxes, and then guess the contents of an unopened box. Then she leaves, the boxes are closed, and Bob enters, opens some but not all boxes, and guesses the contents of an unopened box. Alice and Bob can plan their strategies before the game, but once the game starts they cannot communicate. Paradoxical Theorem: There are strategies that Alice and Bob can follow that guarantee that at least one of them will guess correctly. The proof uses the axiom of choice, but a recent theorem of Elliot Glazer calls into question whether the axiom of choice can be blamed for the paradox.
Abstract: Recovering an unknown function from point samples is an ubiquitous task in various applicative settings: non-parametric regression, machine learning, reduced modeling, response surfaces in computer or physical experiments, data assimilation and inverse problems. In this lecture we discuss the context where the user is allowed to select the measurement points (sometimes refered to as active learning). This allows us to define a notion of optimal sampling point distribution when the approximation is searched in a arbitrary but fixed linear space of finite dimension and computed by weigted-least squares. Here optimal means that the approximation is comparable to the best possible in this space, while the sampling budget only slightly exceeds the dimension. We present simple randomized strategies that provably generate optimal samples, and discuss several ongoing developments.
Abstract: A cryptographic proof of quantumness is a hypothetical test that could be used to prove a quantum computational advantage based on hardness assumptions from cryptography. An experimental realization of such a test would be a major milestone in the development of quantum computation. However, error tolerance is a persistent challenge for implementing such tests: we need a test that not only can be passed by an efficient quantum prover, but one that can be passed by a prover that exhibits a certain amount of computational error. In this talk I will present a technique for improving the error-tolerance in a cryptographic proof of quantumness. The technique is based on hiding a Greenberger-Horne-Zeilinger (GHZ) state within a sequence of classical bits. After giving an overview of this new approach, I will discuss one of the central tools used in the security proof: a strengthened uncertainty principle for the discrete Fourier transform.
Reference: C. Miller, "Hidden-State Proofs of Quantumness," https://arxiv.org/abs/2410.06368
Abstract: It was conjectured by Alon in the 1980s that random d-regular graphs have the largest possible spectral gap (up to negligible error) among all d-regular graphs. This conjecture was proved by Friedman in 2004 in major tour de force. In recent years, deep generalizations of Friedman's theorem, such as strong convergence of random permutation matrices due to Bordenave and Collins, have played a central role in a series of breakthrough results on random graphs, geometry, and operator algebras.
In joint work with Chen, Garza-Vargas, and Tropp, we recently discovered a surprisingly simple new approach to such results that is almost entirely based on soft arguments. This approach makes it possible to address previously inaccessible questions: for example, it enables a sharp understanding of the large deviation probabilities in Friedman's theorem, and establishes optimal spectral gaps of random Schreier graphs that require many fewer random bits than ordinary random regular graphs. I will aim to explain some of these results and some intuition behind the proofs.
Abstract: Understanding how to optimally approximate general compact sets by finite dimensional spaces is of central interest for designing efficient numerical methods in forward simulation or inverse problems. While the concept of n-width, introduced in 1936 by Kolmogorov, is well taylored to linear methods, finding the correct analogous concept for nonlinear approximation (which typically occurs when using adaptive methods or neural networks) is still the object of current research. In this talk, we shall discuss a general framework that allows to embrace various concepts of linear and nonlinear widths, present some recent results and relevant open problems.
Abstract: Appealing to the theory of geodesic currents, we will explicitly compute the image of the so-called X-ray transform of a negatively curved manifold. Using this we will prove old and new results about marked length spectrum rigidity of negatively curved metrics.