Towards an Artificial Muse for New Ideas in Physics

Sonderkolloquium mit "Caffeine & Connect"

Mario Krenn

Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding or inspire new surprising ideas. I will talk about how AI can be used as an artificial muse in physics, which suggests surprising and unconventional ideas and techniques that the human scientist can interpret, understand and generalize to its fullest potential [1]. I will focus on AI for the design of new physics experiments, in the realm of quantum-optics [2, 3] and quantum-enhanced gravitational wave detectors [4] as well as super-resolution microscopy [5]. In the end, i will talk about AI-Mandel [6], a closed-loop discovery system that automatically generates new ideas in quantum physics [7] and can execute and verify them with intelligent domain tools, with ideas being at the level of publishable works in quantum physics. [1] Krenn, Pollice, Guo, Aldeghi, Cervera-Lierta, Friederich, Gomes, Häse, Jinich, Nigam, Yao, Aspuru-Guzik, On scientific understanding with artificial intelligence. Nature Reviews Physics 4, 761 (2022). [2] Krenn, Kottmann, Tischler, Aspuru-Guzik, Conceptual understanding through efficient automated design of quantum optical experiments. Physical Review X 11(3), 031044 (2021). [3] Ruiz-Gonzalez, Arlt, et al., Digital Discovery of 100 diverse Quantum Experiments with PyTheus, Quantum 7, 1204 (2023). [4] Krenn, Drori, Adhikari, Digital Discovery of interferometric Gravitational Wave Detectors, in press: Phys. Rev. X 15, 021012 (2025). [5] Rodríguez, Arlt, Möckl, Krenn, Automated discovery of experimental designs in super-resolution microscopy with XLuminA, Nature Comm. 15, 10658 (2024) [6] Arlt, Gu, Krenn, Towards autonomous quantum physics research using LLM agents with access to intelligent tools. arXiv:2511.11752 (2025). [7] Gu, Krenn, Interesting Scientific Idea Generation Using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders. arXiv:2405.17044 (2024)