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 superpolynomial quantum speedup, and the state of the literature thats attempting to bypass these barriers. Along the way, well also see an interesting connection between quantum linear algebra and classical sampling and sketching algorithms (explored in the form of quantuminspired classical algorithms). Learn more online: