  We propose an iterative algorithm for incomplete quantum process tomography,
with the help of quantum state estimation, based on the combined principles of
maximum-likelihood and maximum-entropy. The algorithm yields a unique estimator
for an unknown quantum process when one has less than a complete set of
linearly independent measurement data to specify the quantum process uniquely.
We apply this iterative algorithm adaptively in various situations and so
optimize the amount of resources required to estimate the quantum process with
incomplete data.
