Training a large neural network across hundreds of compute nodes is like making soup as a team: each 'chef' tastes the dish and tells the others what to adjust. In modern machine learning, such 'negotiations' between nodes take significant time, eat up network bandwidth, and are vulnerable to eavesdropping, as if competitors could overhear the secret recipe.

Researchers replaced classical signals with quantum communication based on entanglement. Each computer gets one particle from a pair of entangled particles: these particles behave like two sides of the same coin: changing one instantly determines the other, no matter where it is. The protocol developed by Charles Bennett lets you send two classical bits of information using just one quantum signal — halving the amount of data transmitted. Moreover, any attempt at espionage is immediately detected thanks to fundamental properties of quantum mechanics: the act of measurement destroys the entangled state, leaving an indelible trace. This approach speeds up both classical and quantum neural networks — the soup analogy simplifies things here, since in reality 'chefs' exchange not taste sensations but model parameters, and quantum links make this exchange nearly instantaneous and secure.

After training, you need to check that the result is consistent across all nodes — make sure the 'soup' tastes the same everywhere. The quantum method offers savings that classical approaches can't match: a few quantum signals can replace thousands of classical ones, drastically cutting synchronization overhead.