公开(公告)号：US20220036231A1

公开(公告)日：2022-02-03

申请号：US17501764

申请日：2021-10-14

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Xin WANG ,  Zhixin SONG ,  Guangxi LI

IPC: G06N10/00 ,  G06N3/02 ,  G06K9/62

Abstract: A method and device for processing quantum data are provided, which are related to the field of quantum computation. The method includes: determining a quantum data set and category information characterizing a data type of the quantum data set; applying a local quantum circuit to a quantum data point contained in the quantum data set, wherein the local quantum circuit is obtained after part of qubits are selected from a plurality of qubits contained in a parameterized quantum circuit; acquiring state information of qubits in the local quantum circuit after being applied to the quantum data point through measurement, and taking the state information and the category information as training data for training a classical neural network to obtain a trained classical neural network, wherein a data type of a quantum data set to be processed can be identified by the trained classical neural network.

公开(公告)号：US20230021555A1

公开(公告)日：2023-01-26

申请号：US17945975

申请日：2022-09-15

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Xin WANG ,  Guangxi LI ,  Xuanqiang ZHAO

IPC: G06N10/60

Abstract: A method includes: obtaining training texts; for each of the training texts, performing the following operations: obtaining a word vector of each word in the current training text as a parameter of a first quantum circuit to obtain quantum states; inputting each of the quantum states to second, third, and fourth quantum circuits and performing measurement; calculating one group of weight values corresponding to each word to obtain a feature vector corresponding to the current training text; inputting the feature vector to a neural network model to obtain a prediction value; and determining a value of loss function based on the prediction value and a label value, and adjusting parameters corresponding to the second, third, and fourth quantum circuits and the neural network model based on the value of the loss function.