Learning from Pseudo-Randomness With an Artificial Neural Network - Does God Play Pseudo-Dice?
This work addresses the challenge of distinguishing between quantum and pseudo-randomness, which is an incremental step in testing fundamental differences in randomness.
The authors tackled the problem of decoding hidden correlations in pseudo-random data using a neural network, demonstrating its learning and prediction power in an extremely random environment, though no concrete numbers were provided.
Inspired by the fact that the neural network, as the mainstream for machine learning, has brought successes in many application areas, here we propose to use this approach for decoding hidden correlation among pseudo-random data and predicting events accordingly. With a simple neural network structure and a typical training procedure, we demonstrate the learning and prediction power of the neural network in extremely random environment. Finally, we postulate that the high sensitivity and efficiency of the neural network may allow to critically test if there could be any fundamental difference between quantum randomness and pseudo randomness, which is equivalent to the question: Does God play dice?