User Trajectory Prediction in Mobile Wireless Networks Using Quantum Reservoir Computing

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.