How a Neural Network Learned to See Quantum Foam ⚡ экспресс
In quantum gravity, the spin foam method describes spacetime from quantum 'bricks,' but calculating amplitudes is very expensive. For the Barrett–Crane model, deep neural networks were applied: a classifier predicts whether the result is zero, and a regressor predicts the exact value. The classifier surprised with its ability to generalize far beyond its training, as if guessing invisible patterns. This will speed up computations in the search for the quantum structure of gravity.
The concept of quantum foam dates back to Roger Penrose and Carlo Rovelli: space is like soap foam, where each bubble is a microscopic chunk of reality. These bubbles constantly burst and merge, and to understand how the Universe works at its deepest level, one must compute the probabilities of their interactions. Previously, such calculations took hours even on powerful computers.
Neural networks handled it in milliseconds. The first network, like a meticulous sorter, instantly discards impossible variants (where probability is zero). The second, like an experienced appraiser, gives precise values for the rest. Astonishingly, it correctly filters out zeros even in unfamiliar scenarios—much like a person who has seen a couple of cats recognizes any breed.
So far, this works on a simplified model, but it paves the way to testing hypotheses about the moment of the Big Bang and the structure of curved spacetime inside black holes. Thus, soap foam brings us closer to solving the mystery of the birth of the Universe.
🎯 The idea that space may not be continuous but consists of minuscule grains dates back to the ancient Greek atomists. In modern physics, it was revived by [scientist:John Archibald Wheeler]John Archibald Wheeler[/scientist], who coined the term 'quantum foam'.
🎬 In the novel 'Blindsight', aliens communicate instantaneously thanks to the quantum nature of space. Perhaps understanding quantum foam will turn this science fiction into reality.