##### Department of Mathematics,

University of California San Diego

****************************

### Math 278C: Optimization and Data Science Seminar (CANCELED TODAY)

## Todd Kemp

#### UCSD

## Entanglement of Symmetric Tensors

##### Abstract:

Entanglement is a core concept in quantum mechanics and quantum information theory. Put simply: a tensor is entangled if is not a product state. Measuring precisely how much entanglement a given tensor has is a big question with competing answers in the physics community. One natural measure is the {\bf geometric measure of entanglement}, which is a version of the Hilbert--Schmidt distance of the given tensor from the set of product states. It can also be described as the log of the spectral norm. In 2009, Gross, Flammia, and Eisert showed that, as the mode of the tensor grows, the geometric measure of entanglement of a random tensor is, with high probability, very close to the theoretical maximum. In this talk, I will describe my joint work with Shmuel Friedland on the analogous situation for symmetric tensors. While symmetric tensors are inherently entangled, it turns out their maximum geometric measure of entanglement is exponentially smaller than for generic tensors. Using tools from representation theory and random matrix theory, we prove that, nevertheless, random symmetric tensors are, with high probability, very close to maximally entangled.

Host: Jiawang Nie

### February 28, 2018

### 2:00 PM

### AP&M 7321

****************************