A disrupted learning mechanism in standard quantum systems followed by their self-organizing process

Recently, the fusion between quantum mechanics and machine learning has gained much attention, where classical machine learning algorithms are adapted for quantum computers to significantly amplify data analysis by leveraging the unique effects of quantum reality. In this short paper, by focusing on the quantum trajectories of particles, we find that under general requirements, quantum systems follow a disrupted version of the gradient descent model, a basic machine learning algorithm, where the learning is distorted due to the self-organizing process of the quantum system. Such a learning process is possible only when we assume dissipation, i.e., that the quantum system is open. The friction parameter determines the nonlinearity of the quantum system. We then provide an empirical demonstration of the proposed model.