Quantum research has captivated the world of science in recent years. Physicists have developed powerful ways to control quantum particles such as capturing, holding, and even moving them to a desired location. Until now, a complex and time-consuming process was required to accurately control these particles due to the difficulty of finding the right electromagnetic fields.
However, a team of researchers from Forschungszentrum Jülich and the Technical University of Vienna (TU Wien) have now demonstrated the effectiveness of machine learning in this task. Using algorithms that have been specifically designed for this application, the process of accurately controlling quantum particles could be completed much faster than previously thought possible.
The team developed a neural network tailored to help achieve the desired accuracy with minimal data. This network was tested through a series of experiments using a specially designed camera for measuring the light field. Through trial and error, the algorithms began to imitate the behavior of the physical system in terms of controlling the quantum particles.
By having this control system run in a matter of days, scientists have opened up a window of possibilities in the world of quantum research. This includes widespread applications such as microscopy and quantum simulators, as the study of quantum particles is key to providing a comprehensive understanding of the universe.
The research conducted by this multi-national team, led by Tommaso Calarco from the Peter Grünberg Institute of Forschungszentrum Jülich and Jörg Schmiedmayer of the Vienna Center for Quantum Science and Technology at TU Wien, was published in the scientific journal Physical Review Applied. Their paper shows the potential of artificial intelligence in controlling quantum particles, an important development for further understanding of physics and the universe.
