Quantum Spinor Gases: Universal Relations, Strong Interactions and Machine Learning Investigations

Abstract: Spinor quantum gases with tunable interactions provide a rich playground for studying novel many body phenomena. In my talk, I first discuss universal relations that apply to spinor quantum gases and illustrate the novel behaviour that arises from the spin degrees of freedom. I then focus on the strong interaction limit in one dimensional spinor gases, and show how they can be modeled as an effective spin chain model. As an example of the utility of this model, I demonstrate the existence of dynamical fermionization in spinor gases,  a uniquely one dimensional phenomena where adding spin gives nontrivial contributions to the dynamics of the gas. Finally, I discuss how machine learning methods such as convolutional neural networks (CNNs) can be used to gain physical insights about solutions to the effective spin chain model. Doing so requires concepts from both physics and computer science such as information entropy, quantum entanglement and physical symmetries. I conclude by showing how CNNs' solution to the effective spin chain model is a special case of Maximum Entropy and Correlator Product State ansatzes.