Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications

Nature·May 8

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A quantum enabled method for training neural-network quantum states has been developed for physico-chemical applications. This method, known as variational Monte Carlo, is reportedly polynomially efficient. It appears to be intended for use in physico-chemical applications.

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