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1
Introduction
2
Context
3
Quantizing Algorithms
4
Model
5
Sketching
6
Important Sampling
7
Observations
8
Property
9
Output
10
Error
11
Their Recommendation Systems
12
Singular Value Transformation
13
Threshold Function
14
Lowrank Approximation
15
Sampling
Description:
Explore quantum-inspired classical linear algebra techniques in this 59-minute lecture by Ewin Tang from the University of Washington. Delve into the quantization of algorithms, model sketching, and important sampling as part of the Quantum Wave in Computing Boot Camp. Learn about observations, properties, and error analysis in quantum-inspired systems. Examine recommendation systems, singular value transformation, and threshold functions. Discover the applications of lowrank approximation and sampling in this advanced exploration of quantum-inspired classical computing methods.

Quantum-Inspired Classical Linear Algebra

Simons Institute
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