Talks

Covariant loop quantum gravity, commonly referred to as the spinfoam model, provides a regularization for the path integral formalism of quantum gravity. A 4-dimensional Lorentzian spinfoam model with a non-zero cosmological constant has been developed based on quantum SL(2,C) Chern-Simons theory on a graph-complement three-manifold, combined with loop quantum gravity techniques. In this talk, I will give an overview of this spinfoam model and highlight its inviting properties, namely (1) that it yields finite spinfoam amplitude for any spinfoam graph, (2) that it is consistent with general relativity with a non-zero cosmological constant at its classical regime and (3) that there exists a concrete, feasible and computable framework to calculate physical quantities and quantum corrections through stationary phase analysis. I will also discuss recent advancements in this spinfoam model and explore its potential applications.

In this talk I will discuss the potential of future high-resolution CMB observations to probe structure on sub-galactic scales. In particular, I will discuss how a CMB-HD experiment can measure lensing over the range 0.005 h/Mpc < k < 55 h/Mpc, spanning four orders of magnitude, with a total lensing signal-to-noise ratio from the temperature, polarization, and lensing power spectra greater than 1900. These lensing measurements would allow CMB-HD to distinguish between cold dark matter (CDM) and non-CDM models that can resolve apparent small-scale tensions with CDM. In addition, CMB-HD can distinguish between baryonic feedback effects and non-CDM models due to the different way each impacts the lensing signal. The kinetic Sunyaev-Zel’dovich power spectrum measured by CMB-HD further constrains non-CDM models that deviate from CDM. In sum, future CMB experiments will not only measure traditional cosmological parameters with unprecedented precision, but will also simultaneously constrain baryonic physics and dark matter properties that impact sub-galactic scales.

Hawking’s seminal result, that black holes behave as black bodies with a non-vanishing temperature, suggests that black holes should evaporate. However, Hawking’s derivation is incomplete, as it neglects the backreaction between radiation and geometry. In this talk, we will present a novel approach to black hole perturbation theory that incorporates backreaction and is valid to arbitrary order. The applications to the physics of evaporating black holes is discussed, and we explore potential experimental implications. The intention is to eventually derive corrections to semi-classical computations in the literature and to determine the fate of evaporating black holes.

The underlying holomorphic structure of a generalized Kahler manifold has been recently understood to be a square in the double category of holomorphic symplectic groupoids (or (1,1)-shifted symplectic stacks). I will explain what this means and how it allows us to describe the generalized Kahler metric in terms of a single real scalar function, resolving a conjecture made by physicists Gates, Hull, and Rocek in 1984. This is based on joint work with Yucong Jiang and Daniel Alvarez available at https://arxiv.org/abs/2407.00831.

Understanding quantum noise is an essential step towards building practical quantum information processing systems. Pauli noise is a useful model widely applied in quantum benchmarking, quantum error mitigation, and quantum error correction. Despite previous research, the problem of how to learn a Pauli noise model self-consistently, completely, and efficiently has remained open. In this talk, I will introduce a framework of gate-set Pauli noise learning that aims at addressing this problem. The framework treats initialization, measurement, and a set of quantum gates to suffer from unknown Pauli noise channels, which are allowed to have customized locality constraints. The goal is to learn all the Pauli noise channels using only those noisy operations. I will first introduce a theory on the “learnability” of Pauli noise model, i.e., what information is fundamentally identifiable within the model and what is not. This is established using tools from algebraic graph theory and ideas from gate set tomography; I will then discuss a sample-efficient procedure to learn all learnable information of a Paul noise model to any desired precision; Finally, I will demonstrate how to apply our theoretic framework for concrete practical gate set and noise assumptions, and discuss the potential impact on quantum error mitigation and other applications.