  This paper applies a quantum machine learning technique to predict mobile
users' trajectories in mobile wireless networks using an approach called
quantum reservoir computing (QRC). Mobile users' trajectories prediction
belongs to the task of temporal information processing and it is a mobility
management problem that is essential for self-organizing and autonomous 6G
networks. Our aim is to accurately predict the future positions of mobile users
in wireless networks using QRC. To do so, we use a real-world time series
dataset to model mobile users' trajectories. The QRC approach has two
components: reservoir computing (RC) and quantum computing (QC). In RC, the
training is more computational-efficient than the training of simple recurrent
neural networks (RNN) since, in RC, only the weights of the output layer are
trainable. The internal part of RC is what is called the reservoir. For the RC
to perform well, the weights of the reservoir should be chosen carefully to
create highly complex and nonlinear dynamics. The QC is used to create such
dynamical reservoir that maps the input time series into higher dimensional
computational space composed of dynamical states. After obtaining the
high-dimensional dynamical states, a simple linear regression is performed to
train the output weights and thus the prediction of the mobile users'
trajectories can be performed efficiently. In this paper, we apply a QRC
approach based on the Hamiltonian time evolution of a quantum system. We
simulate the time evolution using IBM gate-based quantum computers and we show
in the experimental results that the use of QRC to predict the mobile users'
trajectories with only a few qubits is efficient and is able to outperform the
classical approaches such as the long short-term memory (LSTM) approach and the
echo-state networks (ESN) approach.
