Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.
Please help me write a proper abstract based on the patent claims. CLAIM: 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; formulating an objective function based on the data set via the non-quantum processor, wherein the objective function includes a loss term to minimize difference between a first representation of the data set and a second representation of the 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 includes a matrix of real values having a number of columns each defining a vector that corresponds to a qubit in the quantum processor, wherein any of the vectors that correspond to unconnected qubits in the quantum processor are orthogonal to each other; and interacting with the quantum processor, via the non-quantum processor, to minimize the objective function. 2. The method of claim 1 wherein formulating an objective function includes formulating the objective function where the regularization term is governed by an L0-norm form. 3. The method of claim 1 wherein formulating an objective function includes formulating the objective function where the regularization term is governed by an L1-norm form. 4. The method of claim 1 wherein the regularization term includes a regularization parameter, and formulating an objective function comprises selecting a value for the regularization parameter to control a sparsity of the objective function. 5. The method of claim 1 wherein receiving a data set of data elements at a non-quantum processor comprises receiving image data and audio data. 6. The method of claim 1 wherein interacting with the quantum processor, via the non-quantum processor, to minimize the objective function comprises: optimizing the objective function for the first set of values for the weights in the objective function based on the first set of values for the dictionary. 7. The method of claim 6 wherein optimizing the objective function for a first set of values for the weights includes mapping the objective function to a first quadratic unconstrained binary optimization (“QUBO”) problem and using the quantum processor to at least approximately minimize the first QUBO problem, wherein using the quantum processor to at least approximately minimize the first QUBO problem includes using the quantum processor to perform at least one of adiabatic quantum computation or quantum annealing. 8. The method of claim 6 wherein interacting with the quantum processor, via the non-quantum processor, to minimize the objective function further comprises optimizing the objective function for a second set of values for the weights based on a second set of values for the dictionary, wherein optimizing the objective function for a second set of values for the weights includes mapping the objective function to a second QUBO problem and using the quantum processor to at least approximately minimize the second QUBO problem. 9. The method of claim 6 wherein interacting with the quantum processor, via the non-quantum processor, to minimize the objective function further comprises optimizing the objective function for a second set of values for the dictionary based on the first set of values for the weights, wherein optimizing the objective function for a second set of values for the dictionary includes using the non-quantum processor to update at least some of the values for the dictionary. 10. The method of claim 9 wherein interacting with the quantum processor, via the non-quantum processor, to minimize the objective function further comprises optimizing the objective function for a third set of values for the dictionary based on the second set of values for the weights, wherein optimizing the objective function for a third set of values for the dictionary includes using the non-quantum processor to update at least some of the values for the dictionary. 11. The method of claim 10, further comprising: optimizing the objective function for a tth set of values for the weights, where t is an integer greater than 2, based on the third set of values for the dictionary, wherein optimizing the objective function for a tth set of values for the weights includes mapping the objective function to a tth QUBO problem and using the quantum processor to at least approximately minimize the tth QUBO problem; and optimizing the objective function for a (t+1)th set of values for the dictionary based on the tth set of values for the weights, wherein optimizing the objective function for a (t+1)th set of values for the dictionary includes using the non-quantum processor to update at least some of the values for the dictionary. 12. The method of claim 11, further comprising optimizing the objective function for a (t+1)th set of values for the weights based on the (t+1)th set of values for the dictionary, wherein optimizing the objective function for a (t+1)th set of values for the weights includes mapping the objective function to a (t+1)th QUBO problem and using the quantum processor to at least approximately minimize the (t+1)th QUBO problem. 13. The method of claim 11 wherein optimizing the objective function for a (t+1)th set of values for the dictionary based on the tth set of values for the weights and optimizing the objective function for a (t+1)th set of values for the weights based on the (t+1)th set of values for the dictionary are repeated for incremental values oft until at least one solution criterion is met. 14. The method of claim 13 wherein the at least one solution criterion includes either convergence of the set of values for the weights or convergence of the set of values for the dictionary. 15. The method of claim 1 wherein minimizing the objective function comprises generating features in a learning problem. 16. The method of claim 15 wherein generating features in a learning problem includes generating features in at least one of: pattern recognition problem, training an artificial neural network problem, and software verification and validation problem. 17. The method of claim 15 wherein generating features in a learning problem includes generating features in at least one of a machine learning problem or an application of artificial intelligence. 18. The method of claim 1 wherein minimizing the objective function includes solving a sparse least squares problem. 19. The method of claim 1 wherein setting a first set of values for the dictionary of the objective function comprises: generating a matrix of real values wherein each entry of the matrix is a random number between positive one and negative one; renormalizing each column of the matrix such that a norm for each column is equal to one; and for each column of the matrix, computing the null space of the column; and replacing the column with a column of random entries in the null space basis of the column. 20. The method of claim 1 wherein casting a first set of weights in the objective function as variables using the non-quantum processor comprises casting a first set of weights as Boolean variables using the non-quantum processor. 21. The method of claim 1, further comprising: incorporating at least one label comprised of at least one label element into the data set, wherein the at least one label is representative of label information which logically identifies a subject represented in the data set at an at least an abstract level or category to which the subject represented in the set of data belongs. 22. The method of claim 21 wherein incorporating at least one label comprises incorporating at least one label representative of label information which logically identifies the subject represented in the data set as at least one of an alphanumeric character, belonging to a defined set of humans, a make and/or model of a vehicle, a defined set of objects, a defined foreign or suspect object, or a type of anatomical feature. 23. The method of claim 21 wherein incorporating at least one label comprises incorporating at least one label representative of label information, and the label information is the same type as the corresponding data element. 24. The method of claim 21 wherein receiving a data set of data elements at a non-quantum processor comprises receiving a data set expressed as image data, and the incorporated at least one label element comprises image data. 25. The method of claim 24 wherein incorporating at least one label comprised of at least one label element into the data set comprises incorporating at least one label comprised of at least one label element, the at least one label element comprises image data, and a spatial position of the label element at least partially encodes the label information. 26. The method of claim 21 wherein formulating an objective function comprises formulating an objective function based on both the data set and the incorporated at least one label. 27. The method of claim 1 wherein receiving a data set of data elements at a non-quantum processor comprises receiving a data set expressed as different types or formats of data. 28. The method of claim 1 wherein the objective function is in the form: G(Ŵ,{circumflex over (D)};λ)=Σs=1S∥{right arrow over (z)}s−Σk=1Kwks{right arrow over (d)}k∥2+λΣs=1SΣk=1Kwks. 29. A system to identify maximally repeating patterns in data via Hierarchical Deep Learning (HDL), the system comprising: a quantum processor; a digital processor communicatively coupled with the quantum processor; and a processor-readable storage medium that includes processor-executable instructions to: receive a data set of data elements at a non-quantum processor; formulate an objective function based on the data set via the non-quantum processor, wherein the objective function includes a loss term to minimize a difference between a first representation of the data set and a second representation of the data set, and includes a regularization term to minimize any complications in the objective function; cast a first set of weights in the objective function as variables using the non-quantum processor; set 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 includes a matrix of real values having a number of columns each defining a vector that corresponds to a qubit in the quantum processor, wherein any of the vectors that correspond to unconnected qubits in the quantum processor are orthogonal to each other; and interact with the quantum processor, via the non-quantum processor, to minimize the objective function. 30. A method to identify maximally repeating patterns in data via Hierarchical Deep Learning (HDL), the method comprising: receiving a labeled data set of labeled data elements at a digital processor, each labeled data element which incorporates at least one label comprised of at least one label element; formulating an objective function based on the labeled data set via the digital processor; and interacting with a quantum processor, via the digital processor, to minimize the objective function by: casting a set of weights in the objective function as Boolean variables using the digital processor; setting a first set of values for a dictionary using the digital processor; and optimizing the objective function for a first set of values for the Boolean weights based on the first set of values for the dictionary. 31. The method of claim 30 wherein optimizing the objective function for a first set of values for the Boolean weights includes mapping the objective function to a first quadratic unconstrained binary optimization (“QUBO”) problem and using a quantum processor to at least approximately minimize the first QUBO problem, wherein using the quantum processor to at least approximately minimize the first QUBO problem includes using the quantum processor to perform at least one of adiabatic quantum computation or quantum annealing. 32. The method of claim 31, further comprising optimizing the objective function for a second set of values for the dictionary based on the first set of values for the Boolean weights, wherein optimizing the objective function for a second set of values for the dictionary includes using the digital processor to update at least some of the values for the dictionary. 33. The method of claim 31, further comprising optimizing the objective function for a second set of values for the Boolean weights based on the second set of values for the dictionary, wherein optimizing the objective function for a second set of values for the Boolean weights includes mapping the objective function to a second QUBO problem and using the quantum processor to at least approximately minimize the second QUBO problem. 34. The method of claim 33, further comprising optimizing the objective function for a third set of values for the dictionary based on the second set of values for the Boolean weights, wherein optimizing the objective function for a third set of values for the dictionary includes using the digital processor to update at least some of the values for the dictionary. 35. A processor-readable storage medium comprising processor executable instructions to: receive a data set of data elements at a non-quantum processor; formulate an objective function based on the data set via the non-quantum processor, wherein the objective function includes a loss term to minimize difference between a first representation of the data set and a second representation of the data set, and a regularization term to minimize any complications in the objective function; cast a first set of weights in the objective function as variables using the non-quantum processor; set 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 includes a matrix of real values having a number of columns each defining a vector that corresponds to a qubit in the quantum processor, wherein any of the vectors that correspond to unconnected qubits in the quantum processor are orthogonal to each other; and interact with the quantum processor, via the non-quantum processor, to minimize the objective function. 36. A method of automatically labeling data, the method comprising: receiving unlabeled data in at least one processor-readable storage medium; learning, via at least one processor, a dictionary of dictionary atoms using sparse coding on the received unlabeled data; receiving labeled data elements in the at least one processor-readable storage medium, each labeled data element incorporates at least one respective label comprised of at least one respective label element; reconstructing, via at least one processor, the labeled data using the dictionary to generate encoded labeled data elements; executing, via at least one processor, a supervised learning process using the encoded labeled data elements to produce at least one of a classifier or a label assigner; and storing the produced at least one classifier or label assigner in the at least one processor-readable storage medium. 37. The method of claim 36 wherein executing, via at least one processor, a supervised learning process comprises performing at least one of a perceptron algorithm, a k nearest neighbors (kNN) algorithm, or a linear support vector machine (SVM) with L1 and L2 loss algorithm. 38. The method of claim 36 wherein receiving labeled data elements in the at least one processor-readable storage medium comprises receiving labeled image data elements, each labeled image data element incorporates at least one respective label comprised of at least one respective image label element. 39. The method of claim 36 wherein receiving labeled data elements in the at least one processor-readable storage medium comprises receiving labeled data elements each of a specific type or format of data, and each labeled data element is of the same specific type or format of data as the received respective label element.