  We employ unsupervised learning tools to identify different phases and their
transition in quantum systems subject to the combined action of unitary
evolution and stochastic measurements. Specifically, we consider principal
component analysis and intrinsic dimension estimation to reveal a
measurement-induced structural transition in the data space. We test our
approach on a 1+1D stabilizer circuit and find the quantities of interest
furnish novel order parameters defined directly in the raw data space. Our
results provide a first use of unsupervised tools in dynamical quantum phase
transitions.
