Talks

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.

Quantifying quantum states' complexity is a key problem in various subfields of science, from quantum computing to black-hole physics. I'll explain two approaches to understanding the behavior and the operational significance of quantum complexity in many-body systems. First, I'll consider a simple model on n qubits: We create a random quantum circuit by randomly sampling the gates that compose it. In this model, quantum complexity can be shown to grow linearly in the number of gates until saturating at a value that is exponential in n. This result proves a version of a conjecture by Brown and Susskind in the context of quantum gravity, thereby reinforcing our understanding of the evolution of wormholes in holography. Second, I'll discuss how quantum complexity manifests itself in the operational processes that we can carry out on an n-qubit system. For instance, what resources are necessary to reset an n-qubit memory register to the pure all-zero computational basis state? This approach reveals a connection between thermodynamics and complexity, as we exhibit a trade-off between the thermodynamic work cost that is necessary for the reset procedure and the complexity cost of the procedure. The general trade-off is quantified by a new measure of entropy which directly connects complexity with entropy. I'll discuss the implications of our results and new prospects for many-body physics in the regime where quantum states are of ever increasing complexity.

Joint work with: Jonas Haferkamp, Teja Naga Bhavia Kothakonda, Anthony Munson, Jens Eisert, Nicole Yunger Halpern

Topics will include (but are not limited to): - Quantum error correction in quantum gravity and condensed matter - Quantum information scrambling and black hole information - Physics of random tensor networks and random unitary circuits

The volume of the interior of a two-sided eternal black hole classically grows forever. I will derive a microscopic formula for this volume in JT gravity by summing the non-perturbative contribution of higher topologies. The non-perturbative corrections lead to the saturation of the volume of the interior at times exponential in the entropy of the black hole. I will connect the microscopic formula for the volume with properties of a "thermo-averaged" density matrix, in particular with its second Renyi entropy, which I argue to measure the number of nearly orthogonal states visited by time evolution. I will discuss various problems with this interpretation.

Zoom Link: https://pitp.zoom.us/j/99880539557?pwd=VWZiMFI5Rk5pdnNZRE5kalRvUk1Zdz09

Direct detection searches for dark matter are typically optimized to search for weakly interacting particles with masses on the ~GeV scale. But these experiments are, in principle, sensitive to masses as large as approximately the Planck mass, for cross sections much larger than typical Weak-scale cross sections. However, certain assumptions typically made in direct detection analyses break down at such large cross sections, and must be reexamined. In my talk, I will discuss the theoretical considerations that become important for heavy, strongly interacting dark matter, as well as new types of dark matter search that become possible at such high masses and cross sections.

Zoom Link: https://pitp.zoom.us/j/98807962972?pwd=UXFWLzA0N2dPQ3JrVUo4TStmSGtWUT09

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.

Many key cosmological observables have a built-in symmetry under rescaling of all important length scales in the problem. This scaling symmetry can be used to make partial progress toward a complete resolution of the Hubble tension. To exploit the symmetry while respecting observational constraints, we are naturally led to a “mirror world” dark sector. A successful implementation of this scaling symmetry requires a means of increasing the cosmic photon scattering rate that respects observational bounds on the primordial helium abundance. We discuss different ideas that could in principle achieve this rescaling, and discuss their advantages and drawbacks. We finally present some general observations about the fundamental nature of the “Hubble tension".

Zoom Link: https://pitp.zoom.us/j/99678466262?pwd=VTY4cWNFUERhU08vYi9lT09MZjJkdz09

Topics will include (but are not limited to): - Quantum error correction in quantum gravity and condensed matter - Quantum information scrambling and black hole information - Physics of random tensor networks and random unitary circuits