Piotr Białas, Piotr Korcyl, Tomasz Stebel, Dawid Zapolski
This paper introduces a method using autoregressive neural networks to estimate entanglement entropies in quantum spin chains via Monte Carlo simulations.
Researchers have developed a new technique using artificial intelligence to study quantum systems, specifically quantum spin chains. By using neural networks that can predict sequences of spins, they can estimate important properties of these systems, such as entanglement, which is a measure of quantum connections between particles. The method was tested on a model known as the Ising chain, and it successfully calculated entanglement for small groups of spins. This approach could be applied to other quantum systems and conditions, providing a powerful tool for understanding complex quantum phenomena.
