Quantum Tensor Networks for Material Design and Probabilistic Modelling

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I am exploring the use of quantum tensor networks for physics simulation, specifically with application to optically induced high temperature superconductivity and quantum thermodynamics in the presence of dissipation, and to probabilistic modelling, specifically with application to sequential and often structured data with power-law correlations such as bio-informatics and natural language. The former is motivated by the need to find efficient reduced representations of the exponentially large state spaces available to quantum systems in order to study and design materials with increased energy efficiency. The latter is motivated by resource compression, efficient sampling (generation) and improved generalisation in machine learning and AI. A bridge between both, is striving for more efficient resource use in the energy and information sectors respectively.