Unlocking quantum critical phenomena with physics guided artificial intelligence

Breakthroughs in cold atom experiments, advances in quantum computing, developments in spin liquids, and the proliferating importance of quantum critical phenomena compel the application of machine learning techniques to difficult quantum problems. In an age where data can drive unparalleled discoveries, expensive-to-acquire data such as measurements of quantum computer states or cold atom chains can be used by the community to distill new information. Thus, more effective ways of prediction and distillation are required to efficiently identify the criticality. While many have done this using a classification algorithm, we have pioneered a method to predict quantum critical phenomena using machine learning in the absence of direct exposure to states on either side of the transition by directly predicting the ground state wavefunction. By analyzing the predictions for the total phase space, we can confidently identify the location of criticality from the evolution of the predicted wavefunctions. Through further development, this type of machine could help researchers quickly, and cheaply, identify regions of the phase space that are of the utmost interest.