  We introduce a self-learning tomographic technique in which the experiment
guides itself to an estimate of its own state. Self-guided quantum tomography
(SGQT) uses measurements to directly test hypotheses in an iterative algorithm
which converges to the true state. We demonstrate through simulation on many
qubits that SGQT is a more efficient and robust alternative to the usual
paradigm of taking a large amount of informationally complete data and solving
the inverse problem of post-processed state estimation.
