The way a neural network learns is mathematically identical to a particle exploring every possible path through the universe.
Boltzmann machines are a type of machine learning model that organizes information through layers of hidden nodes. This study proves that these layers are formally equivalent to the path integrals used in quantum mechanics to calculate how particles move. Essentially, an AI finding the best solution to a problem is doing the same math as a photon traveling through space. This collision of fields means we can use tools from quantum physics to build smarter and more efficient AI. It reveals a deep, hidden symmetry between how machines think and how the universe behaves. This could be the starting point for a truly universal theory of intelligence.
Analogy between Boltzmann machines and Feynman path integrals
arXiv · 2301.06217
We provide a detailed exposition of the connections between Boltzmann machines commonly utilized in machine learning problems and the ideas already well known in quantum statistical mechanics through Feynman's description of the same. We find that this equivalence allows the interpretation that the hidden layers in Boltzmann machines and other neural network formalisms are in fact discrete versions of path elements that are present within the Feynman path-integral formalism. Since Feynman paths