Quantum adiabatic machine learning with zooming
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Abstract
Recent work has shown that quantum annealing for machine learning (QAML) can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose a variant algorithm (QAML-Z) that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z increases the performance difference between QAML and classical deep neural networks by over 40% as measured by area under the ROC curve for small training set sizes. Furthermore, QAML-Z reduces the advantage of deep neural networks over QAML for large training sets by around 50%, indicating that QAML-Z produces stronger classifiers that retain the robustness of the original QAML algorithm.