US 12,254,380 B2
Method of stimulating a conditional quantum master equation in a quantum transport process by a recurrent neural network
Xiaoyu Li, Chengdu (CN); Qinsheng Zhu, Chengdu (CN); Yong Hu, Chengdu (CN); Qing Yang, Chengdu (CN); and Junyi Lu, Chengdu (CN)
Assigned to University of Electronic Science and Technology of China, Chengdu (CN)
Appl. No. 17/432,198
Filed by University of Electronic Science and Technology of China, Chengdu (CN)
PCT Filed May 26, 2021, PCT No. PCT/CN2021/095988
§ 371(c)(1), (2) Date Aug. 19, 2021,
PCT Pub. No. WO2022/160528, PCT Pub. Date Aug. 4, 2022.
Claims priority of application No. 202110111596.2 (CN), filed on Jan. 27, 2021.
Prior Publication US 2024/0046129 A1, Feb. 8, 2024
Int. Cl. G06N 3/04 (2023.01); G06N 3/0442 (2023.01); G06N 10/20 (2022.01)
CPC G06N 10/20 (2022.01) [G06N 3/0442 (2023.01)] 10 Claims
OG exemplary drawing
 
1. 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 I LSTM cells arranged in chronological order, and each LSTM cell has an input value xt and an output value ht, and the output value ht will be transferred into the LSTM cell at the next moment, in which there is a parameter (W, b);
replacing the input value xt with a shot noise spectrum 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; replacing the parameter (W, b) with a connection between traces of density matrices in the conditional quantum master equation at t−1 and t; and
training the recurrent neural network by using data of the shot noise spectrum generated in the quantum transport process to achieve the purpose of simulating the conditional quantum master equation, wherein the quantum transport process corresponds to a physically realizable system.