In this intensive course, advanced topics in the wide range of informatics such as mathematical information sciences, intelligence sciences, life-sciences and socio-economic sciences are introduced by visiting lecturers.
The aim of this course is to broaden students' perspectives by lectures of advanced topics by active scientists in the front line.
Note: There is a short manuscript titled “Can Monte Carlo methodology allow us to implement Shor’s algorithm?” prepared by Machida and Shibakov for this course, which can be downloaded at math.tntech.edu/machida/machida-shibakov-2019.pdf. This white paper explains their motivation in organizing this course on quantum computation and Monte Carlo methodology.
Students can obtain knowledge about advanced topics in mathematical information sciences, intelligence sciences, life sciences and socio-economic sciences.
mathematical information sciences, intelligence sciences, life sciences, socio-economic sciences
|Intercultural skills||Communication skills||Specialist skills||Critical thinking skills||Practical and/or problem-solving skills|
Lectures give intensive lectures about selected advanced topics.
|Course schedule||Required learning|
|Class 1||Preliminary discussion for mathematics of qunatum computation, and class organization.||Study of advanced topics|
|Class 2||Physics and mathematics of simple (single qubit) systems.||Study of advanced topics|
|Class 3||Quantum state spaces: tensor products and n qubit systems||Study of advanced topics|
|Class 4||Quantum probability, entanglement, and Bell's theorem||Study of advanced topics|
|Class 5||Quantum state transformations and quantum gates||Study of advanced topics|
|Class 6||Introduction to quantum computation||Study of advanced topics|
|Class 7||Simple quantum algorithms: Deutsch-Jozsa, and Simon's problems||Study of advanced topics|
|Class 8||Shor’s algorithm and quantum state observations||Study of advanced topics|
|Class 9||Introduction to Monte Carlo simulation||Study of advanced topics|
|Class 10||Markov chain Monte Carlo (MCMC) algorithms||Study of advanced topics|
|Class 11||Introduction to Brownian motion||Study of advanced topics|
|Class 12||Introduction to stochastic differential equations (SDE)||Study of advanced topics|
|Class 13||Pitman theorem, Kent characteristic diffusions, and an application of SDE to Monte Carlo simulations||Study of advanced topics|
|Class 14||When do we stop running a diffusion process and declare a sample from a stationary distribution?||Study of advanced topics|
|Class 15||Can Monte Carlo methodology allow us to implement Shor’s algorithm?||Study of advanced topics|
Specified by lecturers
Will be based on exercise and report.
The details will be announced later.