Dequantization and Quantum Advantage in Learning from Experiments - IPAM at UCLA

Dequantization and Quantum Advantage in Learning from Experiments - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM) via YouTube Direct link

Experimental demonstration of advantage

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13 of 20

Experimental demonstration of advantage

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Dequantization and Quantum Advantage in Learning from Experiments - IPAM at UCLA

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  1. 1 Intro
  2. 2 Quantum machine learning & data advantage
  3. 3 A motivating example for data assisted problems
  4. 4 The power of data in quantum machine learning
  5. 5 What kinds of problems are learnable from a little data?
  6. 6 Quantum memory and quantum-enhanced experiments
  7. 7 Quantum memory and quanturn-enhanced experiments
  8. 8 What's the simplest task we can have an advantage on?
  9. 9 The best possible conventional experiments
  10. 10 Summarizing the scale of the separation
  11. 11 Bell measurements as a feature in learning
  12. 12 Imagining and emulating a quantum data pipeline
  13. 13 Experimental demonstration of advantage
  14. 14 We've learned about states... how about processes?
  15. 15 Unsupervised discovery
  16. 16 Unsupervised classification of processes
  17. 17 SWAPs and virtual distillation to the quantum PCA
  18. 18 SWAP and virtual distillation to the quanturn PCA
  19. 19 Recal dequantization
  20. 20 Summary & Outlook

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