NIPS 2009 Demonstration : Binary Classification using Hardware Implementation of Quantum Annealing

Abstract

Previous work has sought the development of binary classifiers that exploit the ability to better solve certain discrete optimization problems with quantum anneal- ing. The resultant training algorithm was shown to offer benefits over competing binary classi- fiers even when the discrete optimization problems were solved with software heuristics. In this progress update we provide first results on training using a physical implementation of quan- tum annealing for black-box optimization of Ising objectives. We successfully build a classifier for the detection of cars in digital images using quantum annealing in hardware. We describe the learning algorithm and motivate the particular regularization we employ. We provide re- sults on the efficacy of hardware-realized quantum annealing, and compare the final classifier to software trained variants, and a highly tuned version of AdaBoost.