QiML 2.0: Speed-Ups, Scalability, and Performance for New Machine Learning Era

Explore the cutting-edge advancements in quantum-inspired machine learning during this one-hour seminar on QiML 2.0. Delve into the quantum approximation methods of 'dequantized algorithms' developed by Ewin Tang and others, which offer significant speed-ups over previous quantum-inspired machine learning studies. Examine applications in recommendation systems, supervised clustering, and principal component analysis. Discover new utilities in accessible quantum systems and complex systems through tensor network approximations. Gain insights into scalability and performance achievements, as well as solutions for larger systems. Learn about the technical benefits leading up to QiML 2.0 and its practical applications in healthcare. Access the leading 2024 Medical GitHub Code and Notebook Repository for hands-on experience in this rapidly evolving field.

QiML 2.0: Speed Ups, Scalability, and Performance for New Machine Learning Era