Graph-Theoretical Approaches for AI-Driven Discovery in Quantum Optics

Emerging findings in the physical sciences frequently present new avenues for AI applications that can enhance its efficiency or broaden its scope, as we demonstrated in our study on quantum optics. We present a method that represents quantum optics experiments as abstract weighted graphs, converting problems that encompass both continuous and discrete elements into purely continuous optimization tasks. This allows efficient use of both gradient-based and neural network methods, circumventing the need for workarounds due to the discrete nature of the problems. The new representation not only simplifies the design process but also facilitates a deeper understanding and interpretation of strategies derived from neural networks.