Colorado School of Mines, Physics Department
Abstract: Experimental quantum simulators have become large and complex enough that discovering new physics from the huge amount of measurement data can be quite challenging, especially when little theoretical understanding of the simulated model is available. Unsupervised machine learning methods are particularly promising in overcoming this challenge. I will review typical unsupervised learning methods and show that they generally only work for learning simple symmetry-breaking quantum phase transitions. I will then show that a more advanced method known as diffusion map, which performs nonlinear dimensionality reduction and spectral clustering of the measurement data, has much better potential for unsupervised learning of complex phase transitions, such as topological phase transitions and many-body localization. This method is readily applicable to many experimental quantum simulators as it only requires measuring each particle in a single and local basis.
Reference: A. Lidiak and Z.-X. Gong, Phys. Rev. Lett. 125, 225701 (2020).
Unless specifically noted, the talks will all be held in CoorsTek 140/150