Hybrid Classical-Quantum Algorithms

Explore hybrid classical-quantum algorithms in this 1-hour 21-minute lecture by Aram Harrow from MIT, delivered at the Quantum Colloquium on January 26th, 2021. Delve into the complementary strengths of quantum and classical computers, focusing on optimization and inference problems. Learn about quantum algorithms, oracle search, big data quantum speedups, and data reduction techniques like coresets. Examine adaptive coresets, saddle-point optimization, and hybrid algorithms for minimax problems. Investigate variational methods for NISQ devices, efficient gradient measurement, and performance guarantees. Gain insights into quantum simulations and the integration of classical and quantum computing paradigms for solving complex problems.

Intro
quantum algorithms models
oracle search?
oracles from classical memories
Big data quantum speedups?
statistics using data
data reduction: "coresets"
learning
adaptive coresets
saddle-point optimization
hybrid algorithm for minimax
variational (NISQ)
efficient gradient measurement
performance guarantees
saddle-point algorithm gradient descent
quantum simulations