Sampling-Based Sublinear Low-Rank Matrix Arithmetic Framework for Dequantizing Quantum Machine Learning
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Intro
Landscape: exponential speedups in quantum machine learning
Main result: quantum-inspired classical SVT
Preliminaries
Oversampling and query access
SQ has block-encoding-like composition properties
Reducing dimensionality to access matrix products
Main theorem: even singular value transformation
Final thoughts
Taught by
Association for Computing Machinery (ACM)