2019 Advanced Topics in Artificial Intelligence S

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Academic unit or major
Graduate major in Artificial Intelligence
Machida Motoya  Alexander Shibakov 
Course component(s)
Day/Period(Room No.)
Intensive ()  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
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Course description and aims

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.

Student learning outcomes

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

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Lectures give intensive lectures about selected advanced topics.

Course schedule/Required learning

  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



Reference books, course materials, etc.

Specified by lecturers

Assessment criteria and methods

Will be based on exercise and report.

Related courses

  • None

Prerequisites (i.e., required knowledge, skills, courses, etc.)



The details will be announced later.

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