Recent Progress in Hamiltonian Learning - IPAM at UCLA
Offered By: Institute for Pure & Applied Mathematics (IPAM) via YouTube
Course Description
Overview
Explore recent advancements in Hamiltonian learning algorithms through this 47-minute conference talk presented by Yu Tong from the California Institute of Technology at IPAM's Quantum Algorithms for Scientific Computation Workshop. Gain an overview of provably efficient algorithms for learning Hamiltonians from real-time dynamics, and delve into the challenges of reaching the Heisenberg limit, the fundamental precision limit imposed by quantum mechanics. Discover how quantum control, conservation laws, and thermalization play crucial roles in achieving this limit. Examine the fundamentally different techniques required to push the boundaries of Hamiltonian learning and consider open problems critical for practical implementation of these algorithms.
Syllabus
Yu Tong - Recent progress in Hamiltonian learning - IPAM at UCLA
Taught by
Institute for Pure & Applied Mathematics (IPAM)
Related Courses
Thermalization in Quantum Chromodynamics - Ab Initio Approaches and Interdisciplinary ConnectionsKavli Institute for Theoretical Physics via YouTube Ergodicity Breaking in Quantum Many-Body Systems
International Centre for Theoretical Sciences via YouTube Turbulence - Arrow of Time and Equilibrium-Nonequilibrium Behaviour
International Centre for Theoretical Sciences via YouTube Modelling Aggregation and Fragmentation Phenomena Using the Smoluchowski Equation by Argya Dutta
International Centre for Theoretical Sciences via YouTube Quantum Thermalization and Many-Body Anderson Localization by David Huse
International Centre for Theoretical Sciences via YouTube