A. Zlokapa, A. Mott, J. Job, J.-R. Vlimant, D. Lidar and M. Spiropulu, “Quantum adiabatic machine learning with zooming.” arXiv:1908.04480 [quant-ph], 2019. (To be published.) https://arxiv.org/abs/1908.04480
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.
quantum-annealing.py: quantum annealing on D-Wave implementing the zooming algorithm
roc-excited-states.py: calculates the area under ROC curve using the excited states from the Gibbs distribution
Data (sig.csv and bkg.csv) can be downloaded from: https://data.caltech.edu/records/1335