A Quantum Leap in Quantum Information
APA
(2025). A Quantum Leap in Quantum Information. SciVideos. https://youtube.com/live/C4Ywi56hxpU
MLA
A Quantum Leap in Quantum Information. SciVideos, Jan. 16, 2025, https://youtube.com/live/C4Ywi56hxpU
BibTex
@misc{ scivideos_ICTS:30735, doi = {}, url = {https://youtube.com/live/C4Ywi56hxpU}, author = {}, keywords = {}, language = {en}, title = {A Quantum Leap in Quantum Information}, publisher = {}, year = {2025}, month = {jan}, note = {ICTS:30735 see, \url{https://scivideos.org/icts-tifr/30735}} }
Abstract
On a microscopic scale, our world is governed by quantum physics. Beyond the fundamental questions and 'mysteries' of quantum mechanics, the ability to control this microscopic realm opens up exciting opportunities for new applications and quantum technologies—potentially more powerful than their classical counterparts. As we celebrate 2025 as the International Year of Quantum Science and Technology, marking 100 years since the formulation of quantum mechanics by Heisenberg and Schrödinger, we also commemorate three decades of progress in quantum information and quantum computing. This talk will provide an overview of quantum information from both conceptual and historical perspectives. We will explore the implementation and applications of quantum computers and simulators, quantum networks, and quantum metrology. Our primary focus will be on quantum optical systems, such as atoms and ions manipulated by laser light—prototypical examples of engineered quantum many-body systems. These systems can be controlled at the level of individual quanta, enabling precise manipulation, engineering, and distribution of quantum entanglement. Topics will include trapped ions as universal quantum processors, as well as digital and analog simulations of strongly correlated quantum matter using Rydberg atoms in tweezer arrays. We will highlight current research examples, such as quantum simulations of lattice gauge theories, the characterization and verification of quantum devices through Hamiltonian and Liouvillian learning, and the development of quantum algorithms for optimizing entanglement in quantum sensors.