  We initiate the study of quantum state tomography with minimal regret. A
learner has sequential oracle access to an unknown pure quantum state, and in
each round selects a pure probe state. Regret is incurred if the unknown state
is measured orthogonal to this probe, and the learner's goal is to minimise the
expected cumulative regret over $T$ rounds. The challenge is to find a balance
between the most informative measurements and measurements incurring minimal
regret. We show that the cumulative regret scales as
$\Theta(\operatorname{polylog} T)$ using a new tomography algorithm based on a
median of means least squares estimator. This algorithm employs measurements
biased towards the unknown state and produces online estimates that are optimal
(up to logarithmic terms) in the number of observed samples.
