Can Boltzmann Machines Discover Cluster Updates ?
This introduces a new research paradigm where AI can inspire human discovery of innovative algorithms, potentially benefiting computational physics and machine learning.
The paper tackled the problem of accelerating Monte Carlo simulations in physics by using Boltzmann machines as generative models, demonstrating that they can discover unknown cluster Monte Carlo algorithms, with concrete examples applied to the classical Ising model.
Boltzmann machines are physics informed generative models with wide applications in machine learning. They can learn the probability distribution from an input dataset and generate new samples accordingly. Applying them back to physics, the Boltzmann machines are ideal recommender systems to accelerate Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann Machines can even discover unknown cluster Monte Carlo algorithms. The creative power comes from the latent representation of the Boltzmann machines, which learn to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four spin plaquette interactions. Our results endorse a fresh research paradigm where intelligent machines are designed to create or inspire human discovery of innovative algorithms.