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A Data-driven Approach to Correlated Quantum Many-Body Problems
The final report of grant FA9550-18-1-0515 details the developments and successes made in the computational challenge posed by quantum many-body problems throughout chemistry, materials science and condensed matter fields of research, as part of the AFOSR computational mathematics programme. The work focused on the development of the Gaussian Process State as a novel, data-driven approach to describing quantum many-body states, their optimization and physical understanding. It has brought together the fields of machine-learning, electronic structure and function optimization in a novel approach to enable beyond state-of-the-art calculations on a number of key correlated quantum systems.
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