Abstract: A method comprising: setting and training a plurality of classifiers for classification of data in two or more classes; defining a cost function with error functions; optimizing weighting factors associated with each classifier of the plurality of classifiers by converting the cost function into an Unconstrained Optimization problem and solving it with a Tensor Network; setting a boosted classifier based on the optimized weighting factors; solving a problem requiring classification of datapoints in a dataset in the two or more classes using the boosted classifier, the problem defining either a configuration or operation of an apparatus or system, or behavior of a process; and determining at least one of: whether a potential anomaly exists in the operation or the behavior; and a configuration of the apparatus or the system intended to at least one of improve the operation and solve the potential anomaly thereof.

Abstract: A computer-implemented method comprising: obtaining an equation with a cost function for minimization related to an optimization problem thereby yielding a cost function equation, the cost function including two or more binary variables, converting the cost function equation into a Boolean formula in Conjunctive Normal; obtaining a Max-Clique problem by processing the Boolean formula; providing the Max-Clique problem to a Gaussian Boson Sampling, GBS, quantum device; and processing light output data of the GBS quantum device so as to find values of the two or more binary variables of the cost function.