Harnessing Data Revolution in Quantum Matter
Eun-Ah Kim, Cornell University, 12:00 EDTAbstract: Our desire to better understand quantum emergence drove efforts in improving computing power and experimental instrumentation dramatically. However, the resulting increase in volume and complexity of data present new challenges. I will discuss how these challenges can be embraced and turned into opportunities by employing principled machine learning approaches. The rigorous framework for scientific understanding we enjoy in physics makes interpretability an essential feature for machine learning to lead to scientific progress. I will discuss our recent results using machine learning approaches designed to be interpretable from the outset. Specifically, I will present discovering order parameters and their fluctuations in voluminous X-ray diffraction data and discovering signature correlations in quantum gas microscopy data as concrete examples.