Talk on Classical and Quantum Information: Exploring the abstract limits of Computation and Cognition by Sushravya GM

Date: 14th, March, 2018

Time: 5:00 pm

Venue: Room 314, Old Acad. Block, IIIT-Bangalore

Abstract:

In the early 20th Century, the world’s scientific community found itself amidst a highly unintuitive and, historically, one of the most unsettling paradigm shifts in the human perception of reality - The replacement of the comfortable Classical world-view by the Quantum notion. As a result, Information Theory which deals with unpredictability, randomness and complexity - the things that were also found to characterize the very nature of Quantum realm - became central to the description of reality. With this new acquaintance between Information Theory and Quantum Mechanics, Information finally escaped it classical confines to accommodate for different types of information, such as, coherent quantum information, entanglement, etc. Classical Information Theory, in this way, was extended to significantly powerful ‘Quantum Information Theory’ to explore rich variety of capabilities allowed by these types of information. One of these capabilities that is of particular interest to us is Quantum Computation, for we wish to know - how, and to what extent, it could be leveraged to break long standing theoretical barriers in classical Computation and modelling Cognition.

The talk is divided into two parts: In the first part, we will have a quick information-theoretic look at the expressiveness of a range of computational models, including, Turing Machines, Neural Nets, Quantum Circuits, etc. The second part of the talk will discuss how Quantum Information unveils some ideas of great significance for modelling Cognition.

About the Speaker:

Sushravya is a researcher at Accenture Artificial Intelligence Lab, Bangalore. In July 2017, she obtained her (Integrated) Master’s Degree in Data Science from IIIT, Bangalore. The focus of her graduate research under Prof. Shrisha Rao, was on building theoretical foundations for end-to-end transfer learning in Deep RL models. She continues this line of research, applying ideas from theoretical physics and engineering mathematics on insights form systems-neuroscience.

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