08-12, 11:15–11:55 (Asia/Yerevan), 213W PAB
Among the most prominent achievements of the quantum computing field is an algorithm known as Grover’s quantum search. This talk focuses on Grover’s algorithm and its applications to machine learning routines. Prior knowledge required is a basic understanding of linear algebra and computer science, and familiarity with the concepts of machine learning.
Recent years have seen a rapid growth of the field of quantum computing. Among the most prominent achievements of the field is an algorithm known as Grover’s quantum search, which is a quantum database search algorithm offering a quadratic speedup over the classical alternatives. In addition to offering an introduction to Grover’s search, this talk focuses on its applications to machine learning routines. Ideally, people attending the talk have a solid background in linear algebra and some hands-on experience with machine learning and would like to learn how the field can benefit from the novel techniques of quantum computation.
Previous knowledge expected
With a background in computer science and physics, I am currently following my academic journey in quantum computing. I have previously worked as a software engineer and gathered some hands-on experience in machine learning. Some of my recent insights include the applications of innovative techniques of quantum computing to machine learning, around which I am going to construct my talk.