Quantum Machine Learning Algorithms with Ewin Tang
EPISODE 246
|
APRIL
1,
2019
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About this Episode
In this special bonus episode of the podcast, we're joined by Ewin Tang, a PhD student in the Theoretical Computer Science group at the University of Washington.
In our conversation, Ewin and I dig into her paper "A quantum-inspired classical algorithm for recommendation systems," which took the quantum computing community by storm last summer. We haven't called out a Nerd-Alert interview in a long time, but this interview inspired us to dust off that designation, so get your notepad ready!
About the Guest
Ewin Tang
University of Washington
Connect with Ewin
Resources
- Paper: A quantum-inspired classical algorithm for recommendation systems
- Paper: Fast Monte-Carlo Algorithms for finding Low-Rank Approximations
- Paper: Competitive Recommendation Systems
- Paper: Quantum Recommendation Systems
- Paper: Quantum Machine Learning Algorithms: Read the Fine Print
- Blog Post: An overview of quantum-inspired classical sampling
- Paper: Quantum-inspired classical sublinear-time algorithm for solving low-rank semidefinite programming via sampling approaches
- Paper: Forrelation: A Problem that Optimally Separates Quantum from Classical Computing
