Quantum Colloquium — On Quantum Linear Algebra For Machine Learning

We will discuss quantum singular value transformation (QSVT), a simple unifying framework for quantum linear algebra algorithms developed by Gilyén et al. 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).

Panel discussion: Scott Aaronson (UT Austin), Andras Gilyen (Caltech), Ravi Kannan (MSR India), Iordanis Kerenidis (Univ Paris & QC-Ware), Patrick Rebentrost (CQT), Umesh Vazirani (UC Berkeley; moderator)