Ray-based classifier apparatus and tuning a device using machine learning with a ray-based classification framework

A ray-based classifier apparatus for tuning a device using machine learning with a ray-based classification framework, the ray-based classifier apparatus comprising:

• a machine learning module in communication with an action-based navigator module and that communicates a device state to the action-based navigator module, the machine learning module comprising:
• a training data generator module that produces fingerprint data;
• and
• a machine learning trainer module in communication with the training data generator module and that receives the fingerprint data from the training data generator module and produces the device state;
• and
• an action-based navigator module in communication with the device and that comprises:
• a charging module in communication with the device and that sets the charging energy for each quantum well of the device and defines a state action for each of the quantum wells by sending charging data to the device;
• a data acquisition module in communication with the device and that acquires state data from the device for a selected state recognizer;
• a data checker module in communication with the data acquisition module and that receives the state data from the data acquisition module and checks quality of the state data;
• and
• a state estimator module in communication with the data checker module and that receives the state data from the data checker module, estimates the state of the device, determines whether to tune the device based on the state data relative to an estimation for the state of the device, and produces charging data and tunes the device according to the charging data based on the number of quantum dots of the device,
• wherein the machine learning module and the action-based navigator module comprise one or more of logic hardware and a non-transitory computer readable medium storing computer executable code.

A process for tuning a device using machine learning with a ray-based classification framework and an autotuning module, the process comprising:

• generating, by a training data generator module using logic hardware, fingerprint data for the device;
• receiving, by a machine learning trainer module, the fingerprint data from the training data generator module;
• performing, by the machine learning trainer module using logic hardware, machine language training and producing a device state of the device from the fingerprint data;
• receiving, by a recognition module, the device state from the machine learning trainer module;
• recognizing, by the recognition module using logic hardware, the state of the device from the device state using a trained deep neural network and producing recognition data based on the device state;
• receiving, by a comparison module, the recognition data from the recognition module;
• comparing, by the comparison module using logic hardware, a target state of the device with the recognition data and producing comparison data as a result of the comparison;
• receiving, by a prediction module, the comparison data from the comparison module;
• producing, by the prediction module using logic hardware, prediction data based on the comparison data;
• receiving, by a gate voltage controller, the prediction data from the prediction module;
• producing, by the gate voltage controller using logic hardware, controller data and device control data based on the prediction data;
• receiving, by the device, the device control data from the gate voltage controller, controlling the device with the device control data to modify the state of the device, and producing device data in response to controlling the device with the device control data;
• receiving, by a measurement module, the controller data from the gate voltage controller and device data from the device;
• producing, by the measurement module using logic hardware, ray-based data based on the controller data and the device data;
• and
• receiving, by the recognition module, the ray-based data from the measurement module and performing recognition on the ray-based data using the device state from the machine learning trainer module.

A process for tuning a device using machine learning with a ray-based classification framework and action-based navigator module, the process comprising:

• generating, by a training data generator module using logic hardware, fingerprint data for the device;
• receiving, by a machine learning trainer module, the fingerprint data from the training data generator module;
• performing, by the machine learning trainer module using logic hardware, machine language training and producing a device state of the device from the fingerprint data;
• setting, by a charging module using logic hardware, the charging energy for each quantum well of the device and defining a state action for each of the quantum wells by sending charging data to the device using logic hardware;
• acquiring, by a data acquisition module using logic hardware, state data from the device for a selected state recognizer;
• receiving, by a data checker module in communication with the data acquisition module, the state data from the data acquisition module and checking quality of the state data;
• and
• receiving, by a state estimator module in communication with the data checker module and the machine learning trainer module, the state data from the data checker module and the device state from the machine learning trainer module;
• estimating, by the state estimator module using logic hardware, the state of the device, determining whether to tune the device based on the state data relative to an estimation for the state of the device, and producing charging data and tuning the device according to the charging data based on the number of quantum dots of the device.