  We provide an adaptive learning algorithm for tomography of general quantum
states. Our proposal is based on the simultaneous perturbation stochastic
approximation algorithm and is applicable on mixed qudit states. The salient
features of our algorithm are efficient ($O \left( d^3 \right)$)
post-processing in the dimension $d$ of the state, robustness against
measurement and channel noise, and improved infidelity performance as compared
to the contemporary adaptive state learning algorithms. A higher resilience
against measurement noise makes our algorithm suitable for noisy
intermediate-scale quantum applications.
