Dr Bruno Bertini’s work is centred around studying the evolution of quantum systems consisting of many particles before they have reached an equilibrium state. The complex, changing interactions between these particles require special approaches to understand and solve.

What is the biggest challenge you face in your work?

Investigating these systems can be divided into two parts: understanding the eventual equilibrium state that is reached, and under- standing the dynamics. The most challenging part is to develop an efficient way to under- stand this far-from-equilibrium situation. Numerical methods struggle, so the idea
here is to come up with systems that are still complex, still interacting, but simple enough to be treated analytically. This can be achieved with tools from tensor network theory, but also some aspects of integrability theory.

What is integrability, and how is it important to your work?

Imagine you have a bunch of particles that are interacting. Integrability is a property according to which you can always separate the inter- actions between these many particles into

a collection of two-body scatterings. By not specifying how this separation must be done, I’m effectively saying that all the possible ways to do that need to be equivalent. As a result, these systems have many more conserva- tion laws than normal systems, which only conserve, for example, energy and momentum.

How can we relate your work to magnetism?

The simplest model that you can have for a magnet in one dimension is the famous Hei- senberg model, which is an integrable model, so it can be treated with some of the methods that I work with. A good number of the systems I study are actually good models for mag- netism, at least in one dimension.

What are some surprising collaborations you’ve engaged in?

Today’s experiments can quite precisely realise the simplified models that I am studying. All the work that has been done on quantum comput- ers is very closely related to the systems that we studied. In this way, we can actually use

our predictions to test quantum computers. I didn’t expect that. Normally, I am focused on more mathematical aspects of the problem with exact solutions, and I didn’t think that these solutions could be used in practice for something so concrete as realising quantum computers.

What are the key questions to answer in the next 10 years?

I hope that in the next 10 years, we can make progress on one big question related to quantum computers. The problem now is that they are noisy. To fully exploit the properties of quantum mechanics in a computer, we need to be able to remove this noise. This is a very big, deep theoretical question. So that’s something very interesting to me as well.