Faculty

John Wright, Massachusetts Institute of Technology (MIT)

In the area of quantum state learning, one is given a small number of "samples" of a quantum state, and the goal is use them to determine a feature of the state. Examples include learning the entire state ("quantum state tomography"), determining whether it equals a target state ("quantum state certification"), or estimating its von Neumann entropy. These are problems which are not only of theoretical interest, but are also commonly used in current-day implementation and verification of quantum technologies.

Joel Wallman, University of Waterloo

Significant global efforts are currently underway to build quantum computers. The two main goals for near-term quantum computers are finding and solving interesting problems in the presence of noise and developing techniques to mitigate errors. In this talk, I will outline and motivate an abstraction layer needed to reliably operate quantum computers under realistic noise models, namely, a cycle consisting of all the primitive gates applied to a quantum computer within a specified time period.

Kun Fang, University of Cambridge

As a more general form of quantum superposition, quantum coherence represents one of the most fundamental features that set the difference of quantum mechanics from the classical realm. In this talk, we will use the tool of semidefinite programming to study two fundamental tasks relating quantum coherence, i.e., coherence distillation of quantum states and coherence cost of quantum processes.

Peter Schumacher, Saarland University

We present an efficient gap-independent cooling scheme for a quantum annealer that benefits from finite temperatures.

An introduction to making scientific figures with Illustrator and Blender

Special guest speaker: Christopher Gutierrez, University of British Columbia

Scientific research can be a slow and laborious process. The absolutely final step in the process is to then communicate your exciting scientific findings to other scientists both in and outside of your field. Yet it is often at this final step where the least amount of time is spent.