Special Physics Course Offerings, 2020–2021, Slides of Quantum Mechanics

Download Special Physics Course Offerings, 2020–2021 and more Slides Quantum Mechanics in PDF only on Docsity! Special Physics Course Offerings, 2020–2021 The following special Physics courses will be offered during the 2020–2021 academic year. Each course is described in more detail in the following pages. Autumn 2020: 2 Phys 427/Phys 575, Boris Blinov, Quantum Computing . . . . . . . . . . . . . . . 2 Phys 428, Jerry Miller, Applications of Modern Physics in Medicine . . . . . . . . 3 Phys 434, Miguel Morales, Advanced Data Analysis Techniques for Large Datasets 4 Phys 576/EE 539, Kai-Mei Fu, Introduction to Quantum Optics . . . . . . . . . . 5 Winter 2021: 6 Phys 232, Gerald Seidler, Introduction to Scientfic Instrumentation . . . . . . . . 6 Phys 576A, Xiaodong Xu, Raman Spectroscopy . . . . . . . . . . . . . . . . . . . 7 Phys 578, Marcel den Nijs, Scale Invariance & Topological Phase Transitions . . . 8 Spring 2021: 9 Phys 428/Phys 578, Armita Nourmohammad, Statistical Physics of Living Systems 9 Phys 576, Miguel Morales, Modern Analysis Techniques for Large Data Sets . . . 10 1 Quantum Computing Phys 427/Phys 575, Autumn 2020 Instructor: Boris Blinov Syllabus: Week 1: Brief review of quantum mechanics; qubits and their representations. Week 2: Entanglement. Week 3: Quantum logic gates. Week 4: Quantum computing architectures. Week 5: Quantum algorithms. Exam 1. Week 6: Physical realizations of qubits. Week 7: Quantum information. Week 8: Cryptography, quantum key distribution; teleportation. Week 9: Single photons, EPR pairs. Week 10: Error correction, fault tolerance. Exam 2. Prerequisites: Phys 225 and Phys 227. Textbook: “A Short Introduction to Quantum Information and Quantum Computation” by M. Le Bellac (Cambridge University Press, 2006). This is where most homework problems will come from. Homework: Weekly, graded. Submitted online only, via Canvas dropbox. One late assign- ment (by no more than one week) will be accepted. Exams: Two take-home, 24-hour exams, one in the middle and one at the end of the quarter. No make-up exams. Course grade is 40% HW + 40% each exam = 120%. 2 Introduction to Quantum Optics for Scientists and Engineers Fall 2020, TTh 3:30-5:20 Course number: EE539* Instructor: Kai-Mei Fu Superposition of vacuum and 5-photon state Hofheinz et al., Nature 2009 In the past two decades, the interaction of light and matter has reached an unprecedented level of control, enabling us to begin to realize technologies based on quantum mechanics. This course aims to give students the analytic and computational tools to understand and simulate current state-of-the-art quantum optics experiments. The course consists of • Introduction/review of the quantum mechanics operator formalism (2 weeks) • Non-classical light (2 weeks) • Atom-classical field interaction (2 weeks) • Atom-quantum field interaction (2 weeks) • CQED applications (2 weeks) The coursework consists of 7 problem sets and 1 final presentation. The only requirement for EE539 is a strong background in linear algebra. Quantum mechanics and electromagnetism is helpful, but not necessary. Prior graduate students have come from EE, physics, chemistry, and materials science. Undergraduates should have completed PHY324 and PHYS325, or have permission from the instructor. *Register for PHY576 if EE539 is full. 5 Introduction to Scientific Instrumentation Phys 232, Winter 2021 Instructor: Prof. G. Seidler, seidler@uw.edu 3 credits, remote-only offering Prerequisites: PHYS123, PHYS334 Enrollment limit: 25 Can be substituted for senior lab requirement The design and use of scientific instrumentation is central to the mission of the physical and biological sciences. This involves a journey starting with project definition and then traveling through instrument design, iterative improvement, user interface optimization, experiment design, data collection, and statistical analysis, to finally reach conclusion. The purpose of this class is to give an introduction to this process with an emphasis on building core skills in software, computer integration with microprocessor automation, data collection, data analysis and statistics, and hands-on experience with the construction and improvement of apparatus. The first offering of this class will therefore split time between several major components. First, we will emphasize instruction in the Python environment, touching on each of basic programming skills, data presentation, and statistical analysis, all using standard Python classes and libraries. Second, this will be a ‘flipped lab’ where every student will have, in their home study space, their own Arduino microprocessor and associated components needed to implement a transmission spectrophotometer or optical fluorescence spectrometer while using the Python environment to interface the microprocessor via USB port. Third, the students will gain strong skills in data reduction and graphical presentation, enabling effective presentation of experimental results, including (virtual) in-class presentation. The major class project for each student or small collaborative pod will be the iterative development, testing, and application of their spectrometer including its integration with the Python environment to achieve both a complete user interface for data collection and also a well-documented analysis pipeline for data analysis and presentation of results. Grading will be based on homework (70%) and the final course project (30%). Notes: 1) Students will need to have access to a relatively modern computer with a standard operating system allowing installation of conda, Jupyter, and pyFirmata. Students are strongly urged to investigate these constraints well before the Winter 2021 quarter starts. 2) There will be a $50 lab fee, which will cover the Arduino board and all other course-relevant components (such as for the photometer). A ‘kit’ style package will be delivered to each enrolled student. If delivery of such a package will be complicated by customs requirements, please contact the instructor during the Autumn 2020 term. 6 Raman Spectroscopy, Physics 576A Winter 2021 Instructor: Xiaodong Xu This course will cover Raman spectroscopy application in understanding a wide range of material properties. We will learn the basics of group theory, and how to use group theory to count Raman modes and analyze the Raman optical selection rules based on the symmetry of the system. We will then introduce the application of Raman spectroscopy to understand several material properties, including semiconductors, magnets, superconductors, and charge density waves. Students will have opportunity to form a small group to present the application of Raman in system of their own interest. We will also design and perform a group project, using the equipment in the Xu group: Raman optical study of a 2D materials with application of strain. All students will analyze the data and write a report based on the experimental results. Group theory + Application to Raman spectroscopy (week 1-4) • Group theory basics • Raman selection rules (Raman Active, Infrared Active, …) • Modes Assignment Application of Raman (week 5-9, including group presentation) • Magnetic order • Superconductivity • Topological insulator Group project: (Week 1-7) • Raman spectroscopy to investigate 2D materials. We focus on CDW superconductor: NbSe2 (Amplitude+ Higgs mode). We will try to investigate the competition of CDW and superconductivity with strain control. Time line of the project • week 1-2: develop and test strain setup • Week 3 –7: Load strain setup and perform Raman spectroscopy • Week 7-10: Data Analysis and write up the report. Textbook: (1) Group Theory and Quantum Mechanics by Michael Tinkham; (2) Group Theory: Application to the Physics of Condensed Matter by Mildred Dresselhause, Gene Dresselhaus, and Ado Jario. 7 Modern Analysis Techniques for Large Data Sets Phys 576, Spring 2021 Instructor: Miguel Morales While analyzing large datasets is nothing new for physicists, in the last few years there have been major advancements in the tools and techniques available. Team taught by Miguel Morales (Physics) and Bryna Hazelton (eScience), the goal of this class is to introduce students to current techniques and best practices in the statistically rigorous analysis of large data sets. The class is organized around four themes: practical statistics, advanced data visualization, building collaborative analysis code, and advanced data analysis practices (see below for details). The class is open to graduate students, postdocs, research groups, and seniors with permis- sion. Evaluation will be based on homework and projects, and students are encouraged to use their own data for the projects to enhance their current research. Prof. Miguel Morales has experience in particle physics, astrophysics, and cosmology data analysis, and is considered an international expert in the analysis of 21 cm cosmology data. Senior Research Scientist Bryna Hazelton has worked on everything from cosmology to botany to homelessness as part of the eScience Institute. She is a co-author of the open source and peer reviewed pyuvdata software package, and has developed the reference anal- ysis pipeline for analyzing Epoch of Reionization radio cosmology data. Topic list (not in syllabus order): Advanced practical statistics Foundations non-Gaussian and non-analytic statistics Maximum likelihood Feldman-Cousins and extensions Issues with large data sets and trials Practical considerations Determining background distributions from data Systematic errors End-to-end error propagation (including non-Gaussian extensions) Parameters, covariance, Fischer Matrices, non-linear effects, and the art of parametrization Asking statistically valid questions How to mathematically formulate your question(s) Case studies of mistakes in the literature Jackknife and null tests Data visualization Features of high quality visualizations Data density Classes of plots, and their pros and cons Meta information and drillability Scaling & color Animations and movies Developing a consistent visual language Accessibility considerations (e.g. colorblind, pattern recognition, etc.) Visualizations for data exploration and hunting systematics Turning statistical questions into plots 10 Developing plots for data rampages Visualizations for instrument and data monitoring Sparklines, comparisons with nominal performance Notebooks and dashboards Visualizations for presentations and publications Developing plots as a teaching tool Specific concerns for presentations and publication plots Case studies of valuable visualization techniques Tools and best practices for building collaborative analysis pipelines Using GitHub to your advantage Branching and merging for collaborative data analysis Unit testing Git hashes, metadata, and analysis provenance Collaborative development of analysis tools Issue tracking Issue assignment and managing releases Pull requests Shared libraries for enhanced communication Publishing peer reviewed code Advanced data analysis practices Making sure your analysis is right Analysis level unit tests Designing a thicket of tests Tracing your analysis as it evolves The golden master development pattern Analysis jackknifes, and testing below the thermal noise Tiered testing with data as part of the development cycle Improving your analysis (hunting systematics, biases, calibration errors, and subtle analysis mistakes) Turning questions into tests Newtons method of isolating issues Interrogating your data for systematics and biases (question driven data rampages) FAQs: What constitutes a large data set? The short answer is if it is large for you, it counts. What is big data varies wildly by field, but the statistical and analysis issues are effectively the same whether you have 1,000 data points or 1015. What format will the projects take? If you have your own data, the projects will be applying the techniques we learn to your data. And for the final project you will propose what you plan to do, so it should be directly applicable to your research. Is prior knowledge of any particular coding language expected? We are carefully language agnostic. Many examples will be in python, but we frequently use Matlab, IDL, C, and have experience in a variety of other languages. Do the projects and homework in whatever you are comfortable in. Does this count as a graduate distribution requirement? Yes, it should count regardless of your area of study. 11