A Generative Adversarial Network (GAN) is a deep learning architecture that consists of two neural networks competing against each other in a zero-sum game framework. The goal of GANs is to generate new, synthetic data that resembles some known data distribution. Two models are trained simultaneously by an adversarial process, with the generator ("the artist") learning to create images that look real while the discriminator ("the art critic") learns to tell real images apart from fakes.
In other words, two quantum computers playing zero sum monte carlo against each other can quickly compute a near infinite number of possibilities and scenarios and calculate choices, odds, outcomes, and predict future events with high accuracy.
Some videos discussing the subject
THE FINAL LEVEL OF AWAKENING WHO IS THE REAL EVIL (SATAN) https://www.bitchute.com/video/CAZ3Yb7icJGR/
AI: SYNTHETIC OMNISCIENCE GIVING SATAN THE KEY TO THE BOTTOMLESS PIT https://www.bitchute.com/video/C0hHCZ7i9J4A/
In other words, two quantum computers playing zero sum monte carlo against each other can quickly compute a near infinite number of possibilities and scenarios and calculate choices, odds, outcomes, and predict future events with high accuracy.