Quantum Lunch: Quantum Learning Algorithms

Speaker: Alexander Poremba from Heidelberg University

Title: Quantum Learning Algorithms 

Abstract:
In this talk, I will discuss recent developments in quantum learning. The task of learning from noisy samples is especially interesting as an example of near term quantum supremacy for present quantum computing prone to errors. In the presence of noise, quantum learning algorithms often feature surprising speed ups and robustness over classical learning algorithms. 

I will begin with elements of computational learning theory and give examples of recent quantum speed ups.

I will derive a new extension of the well known Bernstein Vazirani algorithm and discuss a related variant that also appeared in independent work on the Learning With Errors problem with quantum samples.