Webinar

Optimal Control of Many-body Quantum Dynamics

Speaker: Rick Mukherjee (Hamburg University, Germany)

Date and time
Webinar link
Meeting number (access code): 2643 105 4488 Meeting password: nYFHQFm3r57

Abstract

The ability to precisely control a quantum system helps us to better understand the subtleties of the microscopic world. A controlled quantum system can provide a suitable platform to study some of the foundational aspects of quantum physics but also paves the way for building the next-generation quantum technology. I will briefly introduce my research interests which largely falls under the umbrella of AMO physics with applications to quantum simulation, metrology and information theory. In the talk, I will proceed to show with specific examples of how controlling a many-body quantum system can be a complex optimization task. I will discuss how techniques developed in machine learning theory are becoming increasingly relevant in quantum control theory that can ultimately help us to study certain emergent phenomena such as generation of long-range order and entanglement in many-body systems.

Rick Mukherjee did his Ph.D at the Max Planck Institute for the Physics of Complex Systems, Germany, where he investigated the benefits of using divalent Rydberg atoms to study strongly correlated many-body systems. This was followed up with two full-time postdoc positions, one at Rice University, USA, and the other at Imperial College London, UK. During his postdoc years, his research primarily focussed on exploring the use of various AMO platforms for quantum simulation and information theory. At Imperial, he applied machine learning techniques to control and optimize many-body quantum dynamics to achieve desired results useful for building quantum technology. Between his postdocs, he has earned visiting fellowships to visit ITAMP Harvard University and IISER Bhopal. Recently, he has been awarded a five-year position at Hamburg University to lead the theory team in building the first scalable neutral-atom based quantum computer for solving optimization problems.