Object detection device incorporating quantum computing and game theoretic optimization and related methods

An object detection device comprising:

• a variational autoencoder (VAE) configured to encode geospatial image data to generate a latent vector, and decode the latent vector to generate new geospatial image data;
• a quantum computing circuit configured to perform quantum subset summing;
• and
• a processor configured to
• generate a game theory reward matrix for a plurality of different deep learning models, the game theory reward matrix including rows corresponding to the different deep learning models, and columns corresponding to predictive deep learning metrics for the different deep learning models,
• cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix,
• select a deep learning model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and
• process the new geospatial image data using the selected deep learning model for object detection.

An object detection method comprising:

• encoding image data to generate a latent vector, and decoding the latent vector to generate new image data, using a variational autoencoder (VAE);
• generating a game theory reward matrix for a plurality of different deep learning models using a processor, the game theory reward matrix including a first arrangement of data corresponding to the different deep learning models, and a second arrangement of data corresponding to predictive deep learning metrics for the different deep learning models;
• performing quantum subset summing of the game theory reward matrix using a quantum computing circuit;
• selecting a deep learning model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix using the processor;
• and
• processing the new image data with the processor using the selected deep learning model for object detection.

An object detection device comprising:

• a variational autoencoder (VAE) configured to encode geospatial image data to generate a latent vector, and decode the latent vector to generate new geospatial image data;
• a quantum computing circuit configured to perform quantum subset summing;
• and
• a processor configured to
• generate a game theory reward matrix for a plurality of different deep learning models,
• cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix,
• select a deep learning model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and
• process the new geospatial image data using the selected deep learning model for object detection.