  In this work the quantum random walk search algorithm with walk coin
constructed by generalized Householder reflection and phase multiplier has been
studied. The coin register is one qudit with arbitrary dimension. Monte Carlo
simulations, in combination with supervised machine learning, are used to find
walk coins making the quantum algorithm more robust to deviations in the coin's
parameters. By applying deep neural network we make prediction for the
parameters of an optimal coin with arbitrary size and estimate the stability
for such coin.
