Courses tagged with "Information environments" (1105)

This course is an introduction to quantum computational complexity theory, the study of the fundamental capabilities and limitations of quantum computers. Topics include complexity classes, lower bounds, communication complexity, proofs, advice, and interactive proof systems in the quantum world. The objective is to bring students to the research frontier.

This course provides an introduction to the theory and practice of quantum computation. Topics covered include: physics of information processing, quantum logic, quantum algorithms including Shor's factoring algorithm and Grover's search algorithm, quantum error correction, quantum communication, and cryptography.

This is an advanced graduate course on quantum computation and quantum information, for which prior knowledge of quantum mechanics is required. Topics include quantum computation, advanced quantum error correction codes, fault tolerance, quantum algorithms beyond factoring, properties of quantum entanglement, and quantum protocols and communication complexity.

6.453 Quantum Optical Communication is one of a collection of MIT classes that deals with aspects of an emerging field known as quantum information science. This course covers Quantum Optics, Single-Mode and Two-Mode Quantum Systems, Multi-Mode Quantum Systems, Nonlinear Optics, and Quantum System Theory.

8.321 is the first semester of a two-semester subject on quantum theory, stressing principles. Topics covered include: Hilbert spaces, observables, uncertainty relations, eigenvalue problems and methods for solution thereof, time-evolution in the Schrodinger, Heisenberg, and interaction pictures, connections between classical and quantum mechanics, path integrals, quantum mechanics in EM fields, angular momentum, time-independent perturbation theory, density operators, and quantum measurement.

8.322 is the second semester of a two-semester subject on quantum theory, stressing principles. Topics covered include: time-dependent perturbation theory and applications to radiation, quantization of EM radiation field, adiabatic theorem and Berry's phase, symmetries in QM, many-particle systems, scattering theory, relativistic quantum mechanics, and Dirac equation.

This subject introduces the key concepts and formalism of quantum mechanics and their relevance to topics in current research and to practical applications. Starting from the foundation of quantum mechanics and its applications in simple discrete systems, it develops the basic principles of interaction of electromagnetic radiation with matter.

Topics covered are composite systems and entanglement, open system dynamics and decoherence, quantum theory of radiation, time-dependent perturbation theory, scattering and cross sections. Examples are drawn from active research topics and applications, such as quantum information processing, coherent control of radiation-matter interactions, neutron interferometry and magnetic resonance.

This course introduces the students to dynamics of large-scale circulations in oceans and atmospheres. Basic concepts include mass and momentum conservation, hydrostatic and geostrophic balance, and pressure and other vertical coordinates. It covers the topics of fundamental conservation and balance principles for large-scale flow, generation and dissipation of quasi-balanced eddies, as well as equilibrated quasi-balanced systems. Examples of oceanic and atmospheric quasi-balanced flows, computational models, and rotating tank experiments can be found in the accompaniment laboratory course 12.804, Large-scale Flow Dynamics Lab.

This class deals with the modeling and analysis of queueing systems, with applications in communications, manufacturing, computers, call centers, service industries and transportation. Topics include birth-death processes and simple Markovian queues, networks of queues and product form networks, single and multi-server queues, multi-class queueing networks, fluid models, adversarial queueing networks, heavy-traffic theory and diffusion approximations. The course will cover state of the art results which lead to research opportunities.

This course investigates the principles of thermal radiation and their applications to engineering heat and photon transfer problems. Topics include quantum and classical models of radiative properties of materials, electromagnetic wave theory for thermal radiation, radiative transfer in absorbing, emitting, and scattering media, and coherent laser radiation. Applications cover laser-material interactions, imaging, infrared instrumentation, global warming, semiconductor manufacturing, combustion, furnaces, and high temperature processing.

This course is an introduction to the basics of random matrix theory, motivated by engineering and scientific applications.

This graduate-level subject explores various mathematical aspects of (discrete) random walks and (continuum) diffusion. Applications include polymers, disordered media, turbulence, diffusion-limited aggregation, granular flow, and derivative securities.

This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.

This seminar aims to develop a teaching knowledge of the field through extensive reading and discussion of major works. The reading covers a broad range of topics - political, economic, social, and cultural - and represents a variety of historical methods.

In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community: randomized methods for deterministic optimization. In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention.

This half-semester course introduces and surveys the major public capital market real estate vehicles, REITs and MBS (with primary emphasis on CMBS). Some background is also included in basic modern portfolio theory and equilibrium asset pricing. This course is primarily designed to provide MSRED students with a basic introduction to the public capital market sources of financial capital for real estate, and how those markets value such capital investments.

This course, offered by the MIT Center for Real Estate, focuses on developing an understanding of the macroeconomic factors that shape and influence markets for real property. We will develop the theory of land markets and locational choice. The material covered includes studies of changing economic activities, demographic trends, transportation and local government behavior as they affect real estate.

This course is an introduction to the most fundamental concepts, principles, analytical methods and tools useful for making investment and finance decisions regarding commercial real estate assets. As the first of a two-course sequence, this course will focus on the basic building blocks and the "micro" level, which pertains to individual properties and deals.

To provide instruction and dialog on practical ethical issues relating to the responsible conduct of human and animal research in the brain and cognitive sciences. Specific emphasis will be placed on topics relevant to young researchers including data handling, animal and human subjects, misconduct, mentoring, intellectual property, and publication.

This course explores the detection and measurement of radio and optical signals encountered in communications, astronomy, remote sensing, instrumentation, and radar. Topics covered include: statistical analysis of signal processing systems, including radiometers, spectrometers, interferometers, and digital correlation systems; matched filters and ambiguity functions; communications channel performance; measurement of random electromagnetic fields, angular filtering properties of antennas, interferometers, and aperture synthesis systems; and radiative transfer and parameter estimation.