A research team led by the Duke Quantum Center used a quantum simulator to observe the so-called “string breaking” dynamics associated with the creation of particles and antiparticles. The work, published September 23 in Nature Physics, shows how trapped-ion quantum computers can be used to study complex processes in fundamental physics.
The scientists modeled a situation in which two bound components of matter move away from each other, while the energy accumulating in the connection reaches a level at which new particle pairs can emerge as it breaks. This process is related to the behavior of quarks, which are not normally observed in isolation and remain bound to one another.
For the experiment, the researchers encoded the string-breaking model in a chain of 13 trapped ions, controlling their interactions with laser beams. They initially drove the system out of equilibrium, then tracked its evolution and detected the emergence of effective charges.
The experimental results were compared with calculations performed on a classical computer, and the two approaches agreed with each other. Although problems of this size can still be solved using classical computers, the researchers expect that larger and more complex quantum systems could make it possible to study problems that are too difficult for classical machines.
Similar string-breaking models have recently been reproduced by other groups using superconducting circuits and neutral atoms. Results from different quantum platforms make it possible to compare their capabilities and limitations.
An interesting aspect of this experiment is that the researchers are not trying to directly reproduce the extreme conditions under which such processes can occur in nature. Instead, they created a controllable quantum system that makes it possible to track similar dynamics and test theoretical ideas experimentally.
The value of quantum computing is particularly visible here in the context of future, larger systems. The results of this experiment can still be compared with calculations from a classical computer, but the aim of the research is to create models whose complexity will make classical computation increasingly difficult as they grow. At the same time, similar results obtained across different platforms provide an important opportunity to understand which technological approaches are most suitable for studying such problems.
