Variational quantum anomaly detection: Unsupervised mapping of phase diagrams on a physical quantum computer

Abstract

One of the most promising applications of quantum computing is simulating quantum many-body systems. However, there is still a need for methods to efficiently investigate these systems in a native way, capturing their full complexity. Here we propose variational quantum anomaly detection, an unsupervised quantum machine learning algorithm to analyze quantum data from quantum simulation. The algorithm is used to extract the phase diagram of a system with no prior physical knowledge and can be performed end-to-end on the same quantum device where the system is simulated on. We showcase its capabilities by mapping out the phase diagram of the one-dimensional extended Bose–Hubbard model with dimerized hoppings, which exhibit a symmetry protected topological phase. Further, we show that it can be used with readily accessible devices today by performing the algorithm on a real quantum computer.