1. A method of using a quantum processor to identify maximally repeating patterns in data via Hierarchical Deep Learning (HDL), the method comprising: receiving a data set of data elements at a non-quantum processor; preprocessing the data set of data elements to generate a preprocessed data set; formulating an objective function based on the preprocessed data set via the non-quantum processor, wherein the objective function includes a loss term to minimize difference between a first representation of the preprocessed data set and a second representation of the preprocessed data set, and includes a regularization term to minimize any complications in the objective function; casting a first set of weights in the objective function as variables using the non-quantum processor; setting a first set of values for a dictionary of the objective function using the non-quantum processor, wherein the first set of values for the dictionary is constrained such that the objective function matches a connectivity structure of the quantum processor; and interacting with the quantum processor, via the non-quantum processor, to minimize the objective function.