Quantum Initiative: Quantum Lunch

The Quantum Lunch is regularly held on Thursdays in the Theoretical Division Conference Room, TA-3, Building 123, Room 121.

The organizing committee includes Malcolm Boshier (P-21), Lukasz Cincio (T-4), Diego Dalvit (T-4), Changhyun Ryu (P-21) , Nikolai Sinitsyn (T-4), Rolando Somma (T-4), Yigit Subasi (T-4), Christopher Ticknor (T-1), and Wojciech Zurek (T-4).

For more information, or to nominate a speaker, contact Yigit Subasi or Lukasz Cincio.

To add your name to the Quantum Lunch email list, contact Kacy Hopwood.

Thursday September 7, 2017
12:30 PM - 2:00 PM

Speaker: Juan Carrasquilla (D-Wave systems)

Technical Host: Lukasz Cincio

TOPIC: Machine learning in classical and quantum many-body physics

Abstract
I will discuss how artificial neural networks can be used to identify phases and phase transitions in condensed matter systems via supervised learning. I will show that standard feed-forward neural networks can be trained to detect multiple types of order parameter directly from raw state configurations sampled with Monte Carlo. In addition, they can detect highly non-trivial states such as Coulomb phases, and if modified to a convolutional neural network, topological phases with no conventional order parameter. Furthermore, I will discuss the application of machine learning ideas to quantum systems. In particular, I will demonstrate that convolutional neural networks (CNN) have the potential to represent ground states of quantum many-body systems by showing that the ground state of Kitaev's toric code can be written as a CNN. Lastly, I will briefly show that machine learning devices such as the restricted Boltzmann machine can be efficiently used for quantum state tomography of highly-entangled states in arbitrary dimension.