A novel approach to time series modeling using quantum generative netoworks

Modeling time series in a quantum setting has been proposed using various learning techniques including Quantum Generative Adversarial Network and Quantum Boltzmann Machines. In this work, we introduce a new paradigm of quantum generative algorithm that is inherently quantum native. We model a stochastic time series by a quantum process with the goal of learning the underlying Hamiltonian H, which governs this process. We explore how this technique generalizes to generate new series of the learned processes as well as learn/generate correlations between multivariate time series.