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Mario Szegedy: A New Algorithm for Product Decomposition in Quantum Signal Processing
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Classroom Contents
Quantum Machine Learning Workshop 2018
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- 1 Elad Hazan: Efficient Optimization for Machine Learning: Beyond Stochastic Gradient Descent
- 2 Vedran Dunjko: A Route towards Quantum-Enhanced Artificial Intelligence
- 3 Srinivasan Arunachalam: Strengths and weaknesses of quantum examples for learning
- 4 Fernando Brandao: Quantum Speed-up for SDPs and Kernel Learning
- 5 Furong Huang: Discovery of Latent Factors in High-dimensional Data Using Tensor Methods
- 6 Anupam Praksah: A Quantum Interior Point Method for LPs and SDPs
- 7 Rolando Somma: Quantum Algorithms for Systems of Linear Equations
- 8 Nathan Wiebe: Optimizing Quantum Optimization Algorithms via Faster Quantum Gradient Computation
- 9 Soheil Feizi: Generative Adversarial Networks: Formulation, Design and Computation
- 10 Kristan Temme: Supervised Learning with Quantum Enhanced Feature Spaces
- 11 Norbert Linke: Quantum Machine Learning with Trapped Ions
- 12 Seth Lloyd: Quantum Generative Adversarial Networks
- 13 Scott Aaronson: Gentle Measurement of Quantum States and Differential Privacy
- 14 Mario Szegedy: A New Algorithm for Product Decomposition in Quantum Signal Processing
