  Unitary learning is a backpropagation that serves to unitary weights update
in deep complex-valued neural network with full connections, meeting a physical
unitary prior in diffractive deep neural network ([DN]2). However, the square
matrix property of unitary weights induces that the function signal has a
limited dimension that could not generalize well. To address the overfitting
problem that comes from the small samples loaded to [DN]2, an optical phase
dropout trick is implemented. Phase dropout in unitary space that is evolved
from a complex dropout and has a statistical inference is formulated for the
first time. A synthetic mask recreated from random point apertures with random
phase-shifting and its smothered modulation tailors the redundant links through
incompletely sampling the input optical field at each diffractive layer. The
physical features about the synthetic mask using different nonlinear
activations are elucidated in detail. The equivalence between digital and
diffractive model determines compound modulations that could successfully
circumvent the nonlinear activations physically implemented in [DN]2. The
numerical experiments verify the superiority of optical phase dropout in [DN]2
to enhance accuracy in 2D classification and recognition tasks-oriented.
