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I review a recent approach to connecting quantum gravity and the real world by deconstantizing the constants of nature, and using their conjugate as a time variable. This is nothing but a generalization of unimodular gravity. The wave functions are then packets of plane waves moving in a space that generalizes the Chern-Simons functional. For appropriate states they link up with classical cosmology in the appropriate limit. There are however deviations, namely during the matter to Lambda transition, raising the possibility that quantum gravity could be in action here and now. At the other extreme I show how this approach can be used to resolve the cosmological singularity, and perhaps more.

Zoom Link: https://pitp.zoom.us/j/94010122575?pwd=L291eHNSOG1wZmpCL1lmWHVJaEwwdz09

This course is designed to introduce modern machine learning techniques for studying classical and quantum many-body problems encountered in condensed matter, quantum information, and related fields of physics. Lectures will focus on introducing machine learning algorithms and discussing how they can be applied to solve problem in statistical physics. Tutorials and homework assignments will concentrate on developing programming skills to study the problems presented in lecture.

Topics will include (but are not limited to): Canonical formulation of constrained systems, The Dirac program, First order formalism of gravity, Loop Quantum Gravity, Spinfoam models, Research at PI and other approaches to quantum gravity.

Topics will include (but are not limited to): Canonical formulation of constrained systems, The Dirac program, First order formalism of gravity, Loop Quantum Gravity, Spinfoam models, Research at PI and other approaches to quantum gravity.

This course will introduce some advanced topics in general relativity related to describing gravity in the strong field and dynamical regime. Topics covered include properties of spinning black holes, black hole thermodynamics and energy extraction, how to define horizons in a dynamical setting, formulations of the Einstein equations as constraint and evolution equations, and gravitational waves and how they are sourced.

In this talk, I intend to give a pedagogical, nonetheless biased, introduction to String Cosmology. After briefly reviewing the Lambda-CDM model and motivating inflation, we will remind ourselves that early universe cosmology remains singular and waiting for alternatives. That will be our cue to consider string theory to define our gravity sector and string thermodynamics to define our matter sector. We will learn how that doesn't work unless we consider more string corrections (in fact, an infinity of them!). Once we are stringy enough, we will be able to build a full cosmological model that is non-singular and already poses itself as an alternative to inflation at the background level.

Zoom Link: https://pitp.zoom.us/j/98491655811?pwd=ZzArNjFVMmZIdE1STjd2TVNWSlZtZz09

Population studies of gravitational-wave events allow us to probe stellar evolution and formation mechanisms of compact binaries. Recent work has painted a conflicting portrait of the distribution of black hole spins in merging binaries. In this talk, I describe the emerging picture of black hole spin using observations from LIGO-Virgo–KAGRA with a new spin model. We find evidence for two subpopulations of binary black holes: 1) merging binaries containing black holes with negligible spin, 2) binaries preferentially aligned with the orbital angular momentum and rapidly spinning. These results are consistent with predictions from studies of angular momentum transport in stars and suggest that the subpopulation of spinning black holes may be spun up via tidal interactions. I will also share insights of the binary black hole population using the latest catalogue of gravitational-wave events.

Zoom Link: https://pitp.zoom.us/j/93713298741?pwd=eFo2Q1lSTVBESWcyZXJlcFVKZ2hRQT09

This course is designed to introduce modern machine learning techniques for studying classical and quantum many-body problems encountered in condensed matter, quantum information, and related fields of physics. Lectures will focus on introducing machine learning algorithms and discussing how they can be applied to solve problem in statistical physics. Tutorials and homework assignments will concentrate on developing programming skills to study the problems presented in lecture.

Topics will include (but are not limited to): Canonical formulation of constrained systems, The Dirac program, First order formalism of gravity, Loop Quantum Gravity, Spinfoam models, Research at PI and other approaches to quantum gravity.

Quantum information science was initially motivated by questions about information processing. For example, what are the consequences of quantum mechanics for computation? Or for cryptography? More recently, quantum information has also become a perspective through which we can study questions in theoretical physics more broadly, including in condensed matter and quantum gravity. While quantum information considers the constraints of quantum mechanics, there are additional constraints on information implied by relativity. In particular, it is impossible to send information faster than the speed of light. In this talk, I consider constraints on information processing imposed by quantum mechanics and relativity together, and the consequences of these constraints for quantum gravity. Doing so reveals novel aspects of how gravitational degrees of freedom can be recorded into a quantum mechanical system, and how an extra dimension can be recorded into ``holographic'' field theories.

Zoom Link: https://pitp.zoom.us/j/99249375009?pwd=QWZyZzQrNXJwSGYyYTZheGwwaVRndz09

The sustained storage, transmission, or processing of quantum information will likely be a non-equilibrium process that requires monitoring the system and applying some form of feedback to produce fault-tolerance.  In this talk, I will discuss a class of models based on random quantum circuits with intermediate measurements that display a similar phenomenology to standard models for fault-tolerance, including the existence of a threshold, but with several helpful simplifications.  However, naïve realizations of the threshold require an exponential number of repetitions of the experiment to fully explore the output space of the intermediate measurements.  Recently, it has been proposed that this problem can be circumvented by developing efficient entanglement “decoders” that have close parallels to quantum error correction decoders.  We show how to leverage modern machine learning tools to devise a neural network decoder to detect the phase transition. We then study the complexity and scalability of this approach and discuss how it can be utilized to detect entanglement phase transitions in generic experiments.

Zoom Link: https://pitp.zoom.us/j/99123641139?pwd=VmkyR3BSNWF5bURVYmFVakp0ZkNRZz09

This course will introduce some advanced topics in general relativity related to describing gravity in the strong field and dynamical regime. Topics covered include properties of spinning black holes, black hole thermodynamics and energy extraction, how to define horizons in a dynamical setting, formulations of the Einstein equations as constraint and evolution equations, and gravitational waves and how they are sourced.