A new study from Chalmers University of Technology in Sweden shows that machine learning can become far more efficient when it starts with a built-in understanding of the laws of physics. Researchers found that giving an AI system this foundational knowledge dramatically reduced the time needed to develop advanced optical components used in technologies ranging from quantum computers to camera and eyeglass lenses.
“When we fed the super-brain information about the laws of physics, it immediately got much smarter. Our calculations now take one tenth of the time previously required,” said Philippe Tassin, a professor in the Department of Physics and Astronomy at Chalmers University of Technology.
Tassin’s team works in nanophotonics, a field focused on controlling light at extremely small scales. When light interacts with structures smaller than its wavelength, it can behave very differently than it does on larger scales. However, natural optical materials have limits that restrict how light can be manipulated. To overcome those constraints, the researchers use computer simulations to design artificial optical materials.
These engineered materials could lead to lighter, thinner, and more effective camera and eyeglass lenses. The research may also support future quantum computing technologies. Working with scientists from Chalmers’ Department of Microtechnology and Nanoscience, where Sweden’s first large-scale quantum computer is under development, the team is exploring nanostructured materials that can precisely control the movement of light.
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