Ewin Tang - On quantum linear algebra for machine learning - IPAM at UCLA скачать в хорошем качестве

Ewin Tang - On quantum linear algebra for machine learning - IPAM at UCLA

Recorded 25 January 2022. Ewin Tang of the University of Washington presents "On quantum linear algebra for machine learning" at IPAM's Quantum Numerical Linear Algebra Workshop. Abstract: We will discuss quantum singular value transformation (QSVT), a simple unifying framework for quantum linear algebra algorithms developed by Gilyén, Low, Su, and Wiebe. QSVT is often applied to try to achieve quantum speedups for machine learning problems. We will see the typical structure of such an application, the barriers to achieving super-polynomial quantum speedup, and the state of the literature that’s attempting to bypass these barriers. Along the way, we’ll also see an interesting connection between quantum linear algebra and classical sampling and sketching algorithms (explored in the form of “quantum-inspired” classical algorithms). Learn more online: http://www.ipam.ucla.edu/programs/wor...