Quantum stochastic modelling and tensor networks - Yang, Chengran (Nanyang Technological University, 2020)
Predicting a stochastic process' future lies at the heart of many scientific areas. A predictive model extracts information from a stochastic process' past and uses it to generate future statistics. There has been significant amount of effort expended towards finding optimal predictive models that minimize the required amount of past information. By taking advantage of quantum resources, quantum models have been shown to reduce the memory requirements beyond classical limits. Meanwhile, tensor networks are an extremely useful mathematical tool for understanding quantum many-body systems.