Quantum Computing and Artificial Neural Network Methods in Approximating the Quantum Many‐Body Fermion System for Modern Quantum Chemistry Applications
This review elucidates how recent advances at the intersection of artificial neural networks and quantum computing have evolved, and details the architectural evolution from Restricted Boltzmann Machines to autoregressive models like RNNs and Transformers, which enable exact, uncorrelated sampling and bypass critical bottlenecks.