However, the Hubbard model is deceptively simple. For even a modest number of electrons and cutting-edge computational approaches, the problem requires massive computing power. That’s because when electrons interact, their fates can become quantum mechanically entangled. This means that even once they’re far apart on different lattice sites, the two electrons can’t be treated individually. Therefore, physicists are required to deal with all the electrons at once rather than one at a time. With more electrons, more entanglements crop up, making the formidable computational challenge exponentially harder.
“It’s essentially a machine that has the power to discover hidden patterns. When we saw the result, we said, ‘Wow, this is more than what we expected.’ We were really able to capture the relevant physics..” — Domenico Di Sante
One way of studying a quantum system is by using what’s called a renormalization group. That’s a mathematical apparatus physicists use to look at how the behavior of a system — such as the Hubbard model — changes when researchers modify properties such as temperature or look at the properties on different scales. Unfortunately, a renormalization group that keeps track of all possible couplings between electrons and doesn’t sacrifice anything can contain tens of thousands, hundreds of thousands, or even millions of individual equations that need to be solved. On top of that, the equations are quite tricky: Each represents a pair of electrons interacting.
Di Sante and his colleagues wondered if they could use a machine learning tool known as a neural network to make the renormalization group more manageable. The neural network is like a cross between a frantic switchboard operator and survival-of-the-fittest evolution. First, the machine learning program creates connections within the full-size renormalization group. The neural network then tweaks the strengths of those connections until it finds a small set of equations that generates the same solution as the original, jumbo-size renormalization group. The program’s output captured the Hubbard model’s physics even with just four equations.
“It’s essentially a machine that has the power to discover hidden patterns,” Di Sante says. “When we saw the result, we said, ‘Wow, this is more than what we expected.’ We were really able to capture the relevant physics.”
Training the machine learning program required considerable computational muscle, and the program ran for entire weeks. The good news, Di Sante says, is that now that they have their program coached, they can adapt it to work on other problems without having to start from scratch. He and his collaborators are also investigating just what the machine learning is actually “learning” about the system. This could provide additional insights that might otherwise be hard for physicists to decipher.
Ultimately, the biggest open question is how well the new approach works on more complex quantum systems such as materials in which electrons interact at long distances. In addition, there are exciting possibilities for using the technique in other fields that deal with renormalization groups, Di Sante says, such as cosmology and neuroscience.
Reference: “Deep Learning the Functional Renormalization Group” by Domenico Di Sante, Matija Medvidović, Alessandro Toschi, Giorgio Sangiovanni, Cesare Franchini, Anirvan M. Sengupta and Andrew J. Millis, 21 September 2022, Physical Review Letters.
Di Sante co-authored the new study with CCQ guest researcher Matija Medvidović (a graduate student at