Hybrid quantum computing system for hyper parameter optimization in machine learning

A computer program product for performing optimization of hyper parameters in machine learning, comprising a non-transitory computer-readable storage medium having computer-executable instructions for causing a classical computer apparatus comprising an optimization application to:

• gather one or more data sets associated with an application;
• identify one or more parameters associated with the application;
• construct a machine learning model using a first part of the one or more data sets and the one or more parameters;
• determine one or more conditions associated with optimizing the machine learning model;
• transmit the machine learning model, the one or more data sets, and the one or more conditions to a quantum optimizer;
• wherein the quantum optimizer is configured for:
• in response to receiving the machine learning model, the one or more data sets, and the one or more conditions, retrieving a second part of the one or more data sets;
• computing a set of optimal hyperparameters for the machine learning model based on the second part of the one or more data sets and the one or more conditions;
• and
• transmitting the set of optimal hyperparameters to the optimization application.

A method for performing optimization of hyper parameters in machine learning, comprising:

• gathering, via a classical computer apparatus, one or more data sets associated with an application;
• identifying, via the classical computer apparatus, one or more parameters associated with the application;
• constructing, via the classical computer apparatus, a machine learning model using a first part of the one or more data sets and the one or more parameters;
• determining, via the classical computer apparatus, one or more conditions associated with optimizing the machine learning model;
• transmitting, via the classical computer apparatus, the machine learning model, the one or more data sets, and the one or more conditions to a quantum optimizer;
• in response to receiving the machine learning model, the one or more data sets, and the one or more conditions, retrieving, via the quantum optimizer, a second part of the one or more data sets;
• computing, via the quantum optimizer, a set of optimal hyperparameters for the machine learning model based on the second part of the one or more data sets and the one or more conditions;
• and
• transmitting, via the quantum optimizer, the set of optimal hyperparameters to the optimization application