  High-order Gaussian beams with multiple propagation modes have been studied
for free-space optical communications. Fast classification of beams using a
diffractive deep neural network, D2NN, has been proposed. D2NN optimization is
important because it has numerous hyperparameters, such as interlayer distances
and mode combinations. In this study, we classify Hermite-Gaussian beams, which
are high-order Gaussian beams, using a D2NN, and automatically tune one of its
hyperparameters known as the interlayer distance. We used the tree-structured
Parzen estimator, a hyperparameter auto-tuning algorithm, to search for the
best model. Results indicated that classification accuracy obtained by
auto-tuning hyperparameters was higher than that obtained by manually setting
interlayer distances at equal intervals. In addition, we confirmed that
accuracy by auto-tuning improves as the number of classification modes
increases.
