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Input/output assumptions of QML
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Classroom Contents
A Classical Algorithm Framework for Dequantizing Quantum Machine Learning
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- 1 Intro
- 2 Dequantizing quantum lipear algebra
- 3 Unifying quantum linear algebra
- 4 Matrix notation
- 5 Input/output assumptions of QML
- 6 Powering up classical computation with measurements
- 7 Sample and query access
- 8 Quantum-inspired sketching, aka importance sampling
- 9 Importance sampling can approximate matrix products
- 10 All we need are RUR decompositions
- 11 Main theorem: even singular value transformation
- 12 Proof sketch of main theprem
- 13 Interpreting the even SVT result
- 14 Comparing quantum-inspired SVT to quantum SVT
- 15 Applications
- 16 Implications for exponential speedups in QML
