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Quantum annealing is an emerging technology with the potential to solve someof the computational challenges that remain unresolved as we approach an erabeyond Moore's Law. In this work, we investigate the capabilities of thequantum annealers of D-Wave Systems, Inc., for computing a certain type ofBoolean tensor decomposition called Boolean Hierarchical Tucker Network (BHTN).Boolean tensor decomposition problems ask for finding a decomposition of ahigh-dimensional tensor with categorical, [true, false], values, as a productof smaller Boolean core tensors. As the BHTN decompositions are usually notexact, we aim to approximate an input high-dimensional tensor by a product oflower-dimensional tensors such that the difference between both is minimized insome norm. We show that BHTN can be calculated as a sequence of optimizationproblems suitable for the D-Wave 2000Q quantum annealer. Although currenttechnology is still fairly restricted in the problems they can address, we showthat a complex problem such as BHTN can be solved efficiently and accurately.

Boolean Hierarchical Tucker Networks on Quantum Annealers