Entanglement, causality and energy conditions in QFTs; Simulations of strongly-correlated quantum systems by tensor networks (Feb. 5, 2018)

  • Published: 2018-02-05

Talk 1

Title: Entanglement, causality and energy conditions in QFTs

Speaker: Dr. Huajia WANG (UIUC)

Abstract:

It has become increasingly clear that entanglement is the key to understand many fundamental questions in quantum field theories (QFTs), such as topological order and holography. In this talk, I will discuss some of our recent progresses in proving the quantum energy conditions in QFTs. These are conjectured constraints on energy densities in QFTs that have been difficult to prove using conventional methods. We demonstrate that by probing the entanglement structure of general QFTs, one can construct powerful proofs of the energy conditions. Along the way one also discovers deep connections to quantum information and causality.

Time: 14:00pm, Feb. 5 (Monday), 2018

 

 

Talk 2

Title: Simulations of strongly-correlated quantum systems by tensor networks

Speaker: Dr. Shi-Ju Ran (The Barcelona Institute of Science and Technology)

Abstract:

Tensor network (TN), a young mathematical tool of high vitality and great potential, has been undergoing extremely rapid developments in the last two decades. My research is mainly focused on three parts related to TN: (1) The TN encoding theory, which is a general and unified TN scheme beyond mean-field or perturbation theories, and its applications to simulating quantum many-body systems; (2) Designing quantum entanglement simulators, which is simple and experimentally feasible few-body models to optimally mimic infinite many-body systems; (3) Quantum machine learning by TN, where the information is processed by quantum channels. My ambition with these three directions is to construct open-source or commercialized (classical or even quantum) codes that simulate strongly-correlated many-body systems, and to build up the TN-based “quantum artificial intelligence.

Time: 15:30pm, Feb. 5 (Monday), 2018

Venue: Rm. S401, Kavli ITS meeting room, UCAS Teaching Building

 

 

 

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