Abstract: The disclosure claims a method of stimulating a conditional quantum master equation in a quantum transport process by a recurrent neural network, comprising the following steps of: establishing a recurrent neural network which is a long short term memory network (LSTM), wherein the LSTM comprises TLSTM cells arranged in chronological order, and each LSTM cell has an input value xt and an output value ht, and there is a parameter (W, b) in the LSTM cell; replacing the input value xt with a shot noise spectrum S(?) of the current obtained according to the conditional quantum master equation; replacing the output value ht with a trace of density matrices in the conditional quantum master equation; and replacing the parameter (W, b) with a connection between density matrices in the conditional quantum master equation at imminent moments.

Abstract: The disclosure claims a method of stimulating a conditional quantum master equation in a quantum transport process by a recurrent neural network, comprising the following steps of: establishing a recurrent neural network which is a long short term memory network (LSTM), wherein the LSTM comprises TLSTM cells arranged in chronological order, and each LSTM cell has an input value xt and an output value ht, and there is a parameter (W, b) in the LSTM cell; replacing the input value xt with a shot noise spectrum S(?) of the current obtained according to the conditional quantum master equation; replacing the output value ht with a trace of density matrices in the conditional quantum master equation; and replacing the parameter (W, b) with a connection between density matrices in the conditional quantum master equation at imminent moments.