Biclustering with a quantum annealer

Several problem in Artificial Intelligence and Pattern Recognition are computationally intractable due to their inherent complexity and the exponential size of the solution space. One example of such problems is biclustering, a specific clustering problem where rows and columns of a data-matrix must be clustered simultaneously. Quantum information processing could provide a viable alternative to combat such a complexity. A notable work in this direction is the recent development of the D-Wave computer, whose processor has been designed to the purpose of solving Quadratic Unconstrained Binary Optimization (QUBO) problems. In this paper, we investigate the use of quantum annealing by providing the first QUBO model for biclustering and a theoretical analysis of its properties (correctness and complexity). We empirically evaluated the accuracy of the model on a synthetic data-set and then performed experiments on a D-Wave machine discussing its practical applicability and embedding properties