Improving and Automating Quantum Computers with Machine Learning w/ Michael Biercuk

Speaker: Michael J. Biercuk Host: Zlatko Minev, Ph.D. Title: Improving and Automating Quantum Computers with Machine Learning Abstract: In this talk we will introduce the concept and experimental implementation of machine-learning-driven robust quantum control, providing a pathway to maximizing hardware performance in near term quantum computers, and forming a complement to quantum error correction in future systems. We will present a series of experiments on superconducting and trapped-ion quantum computers demonstrating the utility of machine learning for error-robust gate design, efficient hardware characterization, and fully autonomous gate optimization. Experimental demonstrations begin with model-based optimized gates exhibiting up to 10X reductions in error, drift sensitivity, and device variability. We then present a new technique allowing in-situ characterization of noise during gates using a flexible machine learning package based on convex optimization. Finally we discuss the first experimental