Learning Beyond Stabilizer States - IPAM at UCLA

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.

Daniel Liang - Learning Beyond Stabilizer States - IPAM at UCLA