Jesús Fuentes
Ion-trap Learning
Although there has been a recent surge in attention to machine learning, it is not a new concept. However, its integration with quantum systems promises a significant advancement in computational capabilities. At the core of this integration is the use of ion traps, devices that confine charged particles through dynamic electric or magnetic fields, enabling the manipulation of quantum states. This capability is fundamental to progressing quantum computation and is the basis of our research to achieve quantum machine learning models. Our recent findings demonstrate that by adjusting magnetic or electric fields within ion traps, it is possible to process micro particles in a manner that directly represents machine learning models. Instead of using traditional software-based algorithms, this approach uses the ion trap as the computational entity, executing tasks like regression and classification by utilizing the arrangement and dynamics of its trapped particles. The geometric aspects of the quantum dynamic picture in the ion trap are examined as well.
