Training Quantum Neural Networks with an Unbounded Loss Function - IPAM at UCLA

Training Quantum Neural Networks with an Unbounded Loss Function - IPAM at UCLA

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

The big tradeoff in OML

7 of 16

7 of 16

The big tradeoff in OML

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Training Quantum Neural Networks with an Unbounded Loss Function - IPAM at UCLA

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  1. 1 Overview
  2. 2 A new type of learning
  3. 3 Quantizing a feed-forward neural network
  4. 4 Unitary quantum neural network
  5. 5 Two components of trainings
  6. 6 Barren plateaus in QNNS
  7. 7 The big tradeoff in OML
  8. 8 Circumventing barren plateau
  9. 9 What does classical ML do?
  10. 10 Extended swap test
  11. 11 Learning thermal states
  12. 12 Generative algorithm to thermal state learning
  13. 13 Gradients for thermal state learning
  14. 14 Shallow algorithm
  15. 15 FT algorithm
  16. 16 Avoiding poor initializations

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