  Gate-based quantum computations represent an essential to realize near-term
quantum computer architectures. A gate-model quantum neural network (QNN) is a
QNN implemented on a gate-model quantum computer, realized via a set of
unitaries with associated gate parameters. Here, we define a training
optimization procedure for gate-model QNNs. By deriving the environmental
attributes of the gate-model quantum network, we prove the constraint-based
learning models. We show that the optimal learning procedures are different if
side information is available in different directions, and if side information
is accessible about the previous running sequences of the gate-model QNN. The
results are particularly convenient for gate-model quantum computer
implementations.
