Abstract: The quantum wavefunction presents the ultimate "big data" problem in physics. When many quantum particles interact in a low-temperature material or a quantum computer, the complexity of the quantum state presents a daunting challenge for any classical simulation strategy. Recently, a new computational toolbox based on modern machine learning techniques has been finding its way into the field of condensed matter and quantum information physics. Industry tools adapted from applications in deep learning are being repurposed for training on "images" of microscopic configurations. In this talk, I will review how supervised, unsupervised and reinforcement learning techniques are being adopted into traditional computational methods for studying quantum many-body systems. As an example, I will discuss recent progress on using generative modelling with stochastic neural networks to enhance an experimental cold Rydberg atom quantum simulator.
Events are free and open to the public unless otherwise noted.