  Quantum process learning is emerging as an important tool to study quantum
systems. While studied extensively in coherent frameworks, where the target and
model system can share quantum information, less attention has been paid to
whether the dynamics of quantum systems can be learned without the system and
target directly interacting. Such incoherent frameworks are practically
appealing since they open up methods of transpiling quantum processes between
the different physical platforms without the need for technically challenging
hybrid entanglement schemes. Here we provide bounds on the sample complexity of
learning unitary processes incoherently by analyzing the number of measurements
that are required to emulate well-established coherent learning strategies. We
prove that if arbitrary measurements are allowed, then any efficiently
representable unitary can be efficiently learned within the incoherent
framework; however, when restricted to shallow-depth measurements only
low-entangling unitaries can be learned. We demonstrate our incoherent learning
algorithm for low entangling unitaries by successfully learning a 16-qubit
unitary on \texttt{ibmq\_kolkata}, and further demonstrate the scalabilty of
our proposed algorithm through extensive numerical experiments.
