  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 that 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 exhibits a symmetry protected
topological phase. Further, we show that it can be used with readily accessible
devices nowadays and perform the algorithm on a real quantum computer.
