  Quantum process tomography is an experimental technique to fully characterize
an unknown quantum process. Standard quantum process tomography suffers from
exponentially scaling of the number of measurements with the increasing system
size. In this work, we put forward a quantum machine learning algorithm which
approximately encodes the unknown unitary quantum process into a relatively
shallow depth parametric quantum circuit. We demonstrate our method by
reconstructing the unitary quantum processes resulting from the quantum
Hamiltonian evolution and random quantum circuits up to $8$ qubits. Results
show that those quantum processes could be reconstructed with high fidelity,
while the number of input states required are at least $2$ orders of magnitude
less than required by the standard quantum process tomography.
