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Sampling-Based Sublinear Low-Rank Matrix Arithmetic Framework for Dequantizing Quantum Machine Learning
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- 1 Intro
- 2 Landscape: exponential speedups in quantum machine learning
- 3 Main result: quantum-inspired classical SVT
- 4 Preliminaries
- 5 Oversampling and query access
- 6 SQ has block-encoding-like composition properties
- 7 Reducing dimensionality to access matrix products
- 8 Main theorem: even singular value transformation
- 9 Final thoughts
