[2002.02973] Recurrent Neural Network Wavefunctionscontact arXivarXiv Twitter

A core technology that has emerged from the artificial intelligence revolution is the recurrent neural network (RNN). Its unique sequence-based architecture provides a tractable likelihood estimate with stable training paradigms, a combination that has precipitated many spectacular advances in natural language processing and neural machine translation. This architecture also makes a good candidate for a variational wavefunction, where the RNN parameters are tuned to learn the approximate ground state of a quantum Hamiltonian. In this paper, we demonstrate the ability of RNNs to represent several many-body wavefunctions, optimizing the variational parameters using a stochastic approach. Among other attractive features of these variational wavefunctions, their autoregressive nature allows for the efficient calculation of physical estimators by providing perfectly uncorrelated samples. We demonstrate the effectiveness of RNN wavefunctions by calculating ground state energies, correlation

3 mentions: @tjmlab@MLSTjournal@kaeri17
Date: 2020/02/11 03:50

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@tjmlab Recurrent Neural Network Wavefunctions t.co/23MNaqvIMB 再帰型ニューラルネットネットワーク波動関数法の提案φ(゚Д゚ )フムフム
@MLSTjournal Very interesting new work from @rgmelko and @carrasqu - Recurrent Neural Network Wavefunctions - t.co/qvfGKHTwq6 #NeuralNetworks #MachineLearning #quantum
@kaeri17 Some more opinions on t.co/XfWjIW9UMy. It is nice to have another ansatz based on a neural network with the autoregressive property that enables efficient samplings. But does it help to solve non-stoquastic Hamiltonians? 1/n

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