# Can Boltzmann Machines Discover Cluster Updates ?

## TL;DR

This paper demonstrates that Boltzmann machines can not only learn physical distributions but also discover new cluster Monte Carlo algorithms by leveraging their latent representations, inspiring innovative computational methods.

## Contribution

It reveals that Boltzmann machines can autonomously discover cluster algorithms for Monte Carlo simulations, a novel application beyond traditional generative modeling.

## Key findings

- Boltzmann machines can identify clusters in physical systems.
- They can discover new Monte Carlo algorithms.
- Effective in models with complex interactions.

## Abstract

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.