Non-linear data dependency detection in machine learning using hybrid quantum computing

A system for recommending services and/or products to a user, said system detecting non-linear data dependency using hybrid quantum computing:

• a machine learning algorithm operating on a classical computer, the machine learning algorithm operable to:
• receive a data set comprising a plurality of data elements, said data set corresponding to historical preferences of the user, service and/or product history of the user, preferences of a plurality of users and service and/or product history of the plurality of users;
• identify the plurality of data elements within the data set;
• identify one or more features for each data element included in the plurality of data elements;
• determine a total number of features for the data set;
• input the total number of features into a classical machine learning model;
• and
• output, from the classical machine learning model, a first result set, said first result set comprising one or more services and/or products to recommend to the user;
• a quantum algorithm operating on a hybrid quantum computer, the quantum algorithm operable to:
• receive a selection of an accuracy metric that the services and/or products recommended are appropriate for the user;
• receive the data set;
• identify one or more features for each data element included in the plurality of data elements;
• determine the total number of features for the data set;
• reduce, by a quantum annealing method, based on the accuracy metric, the total number of features to a reduced number of features;
• input the reduced number of features into a quantum machine learning model;
• and
• output, from the quantum machine learning model, a second result set, said second result set comprising one or more services and/or products to recommend to the user;
• and
• a hardware processor operable to:
• compare the first result set comprising one or more services and/or products to recommend to the user to the second result set comprising one or more services and/or products to recommend to the user;
• identify that the first result set as compared to the second result set obtained a result greater than a predetermined threshold degree of similarity;
• and
• recommend, using a user interface, the one or more services and/or products to the user.

A method for three-dimensional image processing, the method involving non-linear data dependency detection using hybrid quantum computing, the method comprising:

• in a first step:
• receiving a data set, the data set comprising a plurality of data elements, said data set corresponding to text files, image files, audio files and audio/visual files for image processing, human identification and three-dimensional computer scene understanding and interpretation, for processing by a machine learning model operating on a machine learning system;
• identifying the plurality of data elements within the data set;
• identifying one or more features for each data element included in the plurality of data elements, the one or more features corresponding to components of the text files, image files, audio files and audio/visual files;
• determining a total number of features for the data set;
• inputting the total number of features into the machine learning model;
• and
• outputting a first result set from the machine learning model, the first result set corresponding to classification of the text files, image files, audio files and audio/visual files individually and as a combination of text files, image files, audio files and audio/visual files;
• in a second step:
• receiving a selection of an accuracy metric, said accuracy metric for pinpointing a degree of accuracy of the classification of the text files, image files, audio files and audio/visual files;
• receiving the data set, for processing by the machine learning model operating on the machine learning system;
• identifying the plurality of data elements within the data set;
• identifying one or more features for each data element included in the plurality of data elements;
• determining the total number of features for the data set;
• reducing, by a quantum annealing method, based on the accuracy metric, the total number of features to a reduced number of features;
• inputting the reduced number of features into the machine learning model;
• and
• outputting a second result set from the machine learning model, the second result set corresponding to classification of the text files, image files, audio files and audio/visual files individually and as the combination of text files, image files, audio files and audio/visual files;
• in a third step:
• comparing the first result set to the second result set;
• identifying that the first result set as compared to the second result set obtained a result greater than a predetermined threshold degree of similarity;
• and
• processing a set of images using the classification of the text files, image files, audio files and audio/visual files individually and the classification of the combination of text files, image files, audio files and audio/visual files.

A hybrid computing method that utilizes a classical computer operating with a graphical processing unit (GPU) and a quantum optimizer to build a model that efficiently performs hyper parameter optimization, the method comprising:

• receiving, at a dependency detection subsystem operating on the classical computer, a set of input parameters and a set of current values assigned to each input parameter included in the set of input parameters, said set of input parameters and set of current values relating to a predetermined classification structure;
• transferring the set of input parameters and the set of current values assigned to each input parameter to the quantum optimizer executing a quantum annealing method;
• identifying, at the quantum optimizer, a set of hyperparameters included in the set of input parameters;
• reducing, at the quantum optimizer, the set of hyperparameters at the quantum optimizer using the quantum annealing method;
• returning the reduced set hyperparameters from the quantum optimizer to the classical computer;
• and
• building, at the classical computer operating with the GPU, a machine learning model using the reduced set of hyperparameters.