Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Learning Beyond Stabilizer States - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM) via YouTube

Overview

Explore a 46-minute conference talk on quantum learning presented by Daniel Liang from Rice University at IPAM's Mathematical Aspects of Quantum Learning Workshop. Delve into the topic of learning beyond stabilizer states, focusing on Clifford circuits and their applications in quantum computing. Discover a new learning algorithm for states produced by Clifford circuits with a small number of T gates, running in polynomial time relative to the number of qubits and exponential time relative to the number of T gates. Learn about an efficient property tester for stabilizer nullity/dimension and the use of Bell difference sampling as a key algorithmic tool. Gain insights into the latest advancements in quantum learning theory and its implications for error correction, quantum key distribution, and classical simulation.

Syllabus

Daniel Liang - Learning Beyond Stabilizer States - IPAM at UCLA

Taught by

Institute for Pure & Applied Mathematics (IPAM)

Reviews

Start your review of Learning Beyond Stabilizer States - IPAM at UCLA

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.