Abstract: A method for training a neural network adapted to generate random numbers and the system thereof is disclosed. The neural network is configured to generate random numbers identical to the distribution of a quantum random number generator based on the received random noise input using a generative adversarial network framework. The method includes feeding random noise to the generator to generate a noisy output. The noisy output is fed to the discriminator along with a real time output of a quantum random number generator to the discriminator. Further, the discriminator is trained to learn the entropy of the real-time output and distinguish it from the noisy output based on the learned entropy. Finally, the output of the discriminator is fed as feedback to the generator.

Abstract: A system for collaborative execution of a task includes a plurality of edge units in communication with a principal processor. Each edge unit includes a collaborative intelligence module configured to establish a communication link amongst at least two of the edge units. The communication intelligence module is further configured to communicate task attributes of the task with at least one other edge unit, and to exchange sensor data from the set of sensors and a plan with the at least one other edge unit to create a joint workspace followed by execution of the task in collaboration with the at least one other edge unit.