The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Quantum Machine Learning: The Next Frontier? with Iordanis Kerenidis - #397
Aug 4, 2020
Iordanis Kerenidis, a Research Director at CNRS Paris and Head of Quantum Algorithms at QC Ware, discusses the groundbreaking realm of quantum machine learning. He shares insights from his keynote at ICML, delving into the evolution of quantum algorithms and their historical milestones. The conversation navigates the intricacies of quantum computing fundamentals, showcasing the power of superposition and its applications in recommendation systems. Iordanis also tackles challenges in integrating quantum methods with classical techniques, revealing the exciting potential and complexities ahead.
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Quantum Computing Paradigm
- Quantum computing is a different paradigm than classical computing, not just a faster processor.
- Design new algorithms to harness quantum mechanics' power for specific tasks, not all.
Quantum Machine Learning Origins
- Quantum machine learning (QML) began around 2009 with the quantum algorithm for linear systems.
- A key QML application is quantum recommendation systems, offering potential speedups compared to classical systems.
Quantum Recommendation System Story
- Iordanis Kerenidis and Anupam Prakash developed a quantum recommendation system algorithm.
- Ewin Tang later developed a faster classical algorithm inspired by it, showing the interplay of quantum and classical ML.