  Estimation of the coin parameter(s) is an important part of the problem of
implementing more robust schemes for quantum simulation using quantum walks. We
present the estimation of the quantum coin parameter used for one-dimensional
discrete-time quantum walk evolution using machine learning algorithms on their
probability distributions. We show that the models we have implemented are able
to estimate these evolution parameters to a good accuracy level. We also
implement a deep learning model that is able to predict multiple parameters
simultaneously. Since discrete-time quantum walks can be used as quantum
simulators, these models become important when extrapolating the quantum walk
parameters from the probability distributions of the quantum system that is
being simulated.
