Applications of Quantum Optics in Experimental Quantum Information
Electronic properties of graphene-based systems
Explore biophysics techniques and models for mapping protein function
Introduction to Lambda-CDM Cosmology
The origin of matter and dark matter in the universe
Relativistic mean field theory and phase transitions in neutron stars
Topological Photonics
Understanding gauge invariance and gauge theories
Deep learning in Physics
Applications of Quantum Optics in Experimental Quantum Information with Vasileios Niaouris
In recent years, we have seen vast growth in the field of quantum information. Various disparate qubit systems (e.g. trapped atoms/ions, superconducting qubits, solid state qubits etc.) have been developed and optimized to push the limits of quantum technologies, such as quantum computing, quantum sensing and quantum communication. One of the best candidates for transfer of information over long distances are photons. Photon-based quantum technologies can be used as quantum memories and quantum repeaters, quantum processors etc. The goal of this course is to acquaint the student with the fundamental physics behind quantum optics and introduce them to real life experimental applications. Depending on student interest and progress, we will focus on either photon-mediated entanglement or optical quantum memories via electromagnetically induced transparency.
Reading list: [1] "Quantum Optics", Scully and Zubairy; Papers: [1] Lijun Ma et al. J. Opt. 19, 043001 (2017), [2] P. Maunz, D. Moehring, S. Olmschenk et al. Nature Phys 3, 538–541 (2007).
Pre-requisites: PHYS 322 (EM 2), PHYS 325 (QM 2)
Electronic properties of graphene-based systems with Chun-Chih Tseng
Graphene has opened Van der Waals (vdW) materials field since its discovery from 2004. Due to the one-atom-thickness and the honeycomb lattice structure, the band structure has unique Dirac cone with linear dispersion in the low energy regime, contributing to various intriguing physics. In this project, we’ll start from the introduction of graphene band structure and electronic properties like qunantum Hall effect. Next, we’ll go through the relevant graphene-based systems to see how people engineer the graphene properties via proximity effect and twist, which is also an important research topic to the date.
Reading list: Selected chapters in "The electronic properties of graphene" by A. H. Castro Neto et al., "The Quantum Hall Effect" by David Tong, and other research articles upon the student's interest
Pre-requisites: PHYS 324 (QM1); Recommended: PHYS 423 (Condensed Matter), PHYS 325 (QM2)
Explore biophysics techniques and models for mapping protein function with Yasmene Wang Elhady
Proteins perform a broad range of functions in our cells. They are involved in cellular signaling, immune responses, motor functions, they facilitate DNA replication and expression, etc. Protein misfolding is believe to be a primary cause of a host of diseases. A quick Google search will list Alzheimer's disease, type 2 diabetes, cancer, Parkinson's disease, cystic fibrosis, Huntington's disease, cataracts, and many others. Biophysicists have contributed to the field of structural proteomics by developing and adopting a broad range of techniques and models to observe and predict how proteins structure themselves and perform their functions. I'd like to do a survey of these techniques and models and get a sense of some of the different angles people are approaching structural proteomics with. If you find a certain angle particularly interesting, we can go deeper and maybe play with some of the math.
Reading list: The reading list will depend on our discussion. As starting off points, we can look Molecular Dynamics Simulation for All by Hollingsworth and Dror, and maybe a couple sections of An Overview of Current Methods to Confirm Protein-Protein Interactions by Kenji Miura or Advancing Biophysics Using DNA Origami by Engelen and Dietz. If particular topics stand out we can look deeper.
Pre-requisites: none
Introduction to Lambda-CDM Cosmology with John Franklin Crenshaw
The "concordance model" of cosmology, Lambda-CDM, has had great success describing the origin, contents, and evolution of the universe. This story begins with the fiery Big Bang, proceeds through structure formation under the influence of Dark Matter, and continues with the modern accelerated expansion driven by Dark Energy. The student will learn the basics of Lambda-CDM cosmology, as well as the open questions and tensions that exist, positioning them to understand the motivations of modern cosmology research.
Reading list:David Tong's Cosmology Notes; Barbara Ryden's Introduction to Cosmology; Wayne Hu's Intermediate Level CMB Tutorial; Sean Carroll's Dark Energy and the Preposterous Universe; plus supplementary material from Ethan Siegel and PBS Space Time
(all sources provided by the DRiP guide)
Pre-requisites: Physics 224, Thermal Physics; Physics 225, Intro to Quantum Mechanics; at least one 300-level physics course
The origin of matter and dark matter in the universe with Huangyu Xiao
In this project, students will go through the fundamentals of two big puzzles in particle physics and cosmology----- dark matter and the asymmetry between matter and anti-matter (baryon asymmetry). The primary goal is to build a understanding of how observations motivate us to propose the existence of dark matter and the baryon asymmetry and what new physics we might need to solve these puzzles.
Reading list: Lectures on Dark Matter Physics: https://arxiv.org/abs/1603.03797; Baryogenesis: https://arxiv.org/abs/hep-ph/0609145
Pre-requisites: Highly recommended: Phys 321 & 322, Phys 324, Phys 226
Relativistic mean field theory and phase transitions in neutron stars with Mia Kumamoto
Relativistic mean field theory is a computationally inexpensive way to make qualitative predictions about dense nuclear matter. These tools can be used to model exotic phases of matter, including kaon condensation and hyperonization. Exotic phases may appear with complicated geometries in neutron stars, significantly changing the equation of state.
Reading list: Compact Stars by Norman Glendenning; Additional papers, depending on time and student's particular interests
Pre-requisites: Phys 325, Phys 328
Topological Photonics with Arnab Manna
The application of topology - the mathematics of conserved properties under continuous deformations, is creating a range of new opportunities throughout photonics. This field was inspired by the discovery of topological insulators, in which interfacial electrons transport without dissipation, even in the presence of impurities. Similarly, the use of carefully designed wavevector-space topologies allows the creation of interfaces that support new states of light with useful and interesting properties such as the ability to propagate around large imperfections without back-reflection. We will first understand the underlying physics and then dig into how topological effects can be realized in photonic crystals, coupled resonators, metamaterials and quasicrystals.
Reading list: Parts of "Topological Photonics", Rev. Mod. Phys. 91(1), 015006 (2019) and references therein
Pre-requisites: PHYS 322, PHYS 325
Understanding gauge invariance and gauge theories with Joseph Merritt
Symmetry has been a key concept in understanding physics for many generations. More recently, the symmetry of a theory under a local transformation has been a driving force for many important conceptual advancements - most notably in high-energy particle theory, but also in fields such as quantum gravity, condensed matter, and even for purely mathematical topics such as topology. We will look at the basics of gauge invariance, what it is, and why it’s necessary. Then we’ll look at different types of gauge invariance, and how to build gauge theories – theories which are designed to have a some chosen gauge invariance. Interest can be directed at continuous gauge groups, which are useful for particle physics; or towards finite gauge groups, which are more applicable to lattice theories and topological phases of matter.
Reading list: Frank Jones’s intro to gauge theory - http://physics.drexel.edu/~bob/TermPapers/Frank_GaugeTheory.pdf; More TBD by student interest
Pre-requisites: Helpful, but not required: PHYS 324 (Quantum Mechanics 1); an interest in group theory.
Deep Learning in Physics with Olivia Thomas
Deep neural networks and other machine learning algorithms are becoming ubiquitous analysis tools across many domains of science. In this DRiP course, we will develop a basic understanding of these tools and learn how to apply them in physics research problems. This course will teach both the theory behind DNNs as well as how to code them yourself. We will also explore the many research questions that scientists are using deep learning to tackle. No prior knowledge of neural networks or machine learning is required!
Reading list: TBD
Pre-requisites: Experience with coding in Python or MATLAB (we will be using Python, however the transition is not hard if you know MATLAB)