FPGA-Based QBoost with Large-Scale Annealing Processor and Accelerated Hyperparameter Search

QBoost is a recently proposed machine learning algorithm, designedto exploit the benefits of emerging annealing processors which solve NP hard problems in combinatorial optimizationa hundred times faster than conventional CPUs. In this paper, we present the first FPGA-based implementation of QBoost, incorporating a large-scale annealing processor with 2704 spins. In contrast to previous implementations, based on quantum annealers, we utilize the flexibility of FPGAs for implementing a fast, integrated QBoost engine which combines the annealing processor and the modules of the hyperparametersearch on a single FPGA. As opposed to quantum annealers, this accelerates the time required for scanning the hyperparameterspace from the order of hours to a single second.