  The existence of "barren plateau landscapes" for generic discrete variable
quantum neural networks, which obstructs efficient gradient-based optimization
of cost functions defined by global measurements, would be surprising in the
case of generic linear optical modules in quantum optical neural networks due
to the tunability of the intensity of continuous variable states and the
relevant unitary group having exponentially smaller dimension. We demonstrate
that coherent light in $m$ modes can be generically compiled efficiently if the
total intensity scales sublinearly with $m$, and extend this result to cost
functions based on homodyne, heterodyne, or photon detection measurement
statistics, and to noisy cost functions in the presence of attenuation. We
further demonstrate efficient trainability of $m$ mode linear optical quantum
circuits for variational mean field energy estimation of positive quadratic
Hamiltonians for input states that do not have energy exponentially vanishing
with $m$.
