PSR-NQS makes recurrent neural quantum states scalable for variational Monte Carlo by using parallel scan recurrence, reaching accurate results on 52x52 two-dimensional lattices.
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Optimizing eigenvalues of the adjacency matrix in linear reservoir computers yields better training and test performance than random linear reservoirs and often beats comparable nonlinear ones.
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Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo
PSR-NQS makes recurrent neural quantum states scalable for variational Monte Carlo by using parallel scan recurrence, reaching accurate results on 52x52 two-dimensional lattices.
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Optimizing the Network Topology of a Linear Reservoir Computer
Optimizing eigenvalues of the adjacency matrix in linear reservoir computers yields better training and test performance than random linear reservoirs and often beats comparable nonlinear ones.