A method for mitigating an error of a quantum circuit that is performed by a processor of a quantum computing device, the method comprising: detecting, by the processor, a quantum circuit to be mitigated among a plurality of quantum circuits, each of the plurality of quantum circuits comprising a combination of a plurality of physical quantum gates; invoking, by the processor, a pre-trained deep learning model for mitigating an error of the detected quantum circuit; inferring, by the processor, an error correction value of the detected quantum circuit by inputting an input data corresponding to the detected quantum circuit to the invoked deep learning model; and mitigating, by the processor, an error of the detected quantum circuit based on the inferred error correction value, wherein, a) the deep learning model is a deep neural network model, the input data of the deep learning model model comprises at least one of information G 1 (a:b) about a number of 1-qubit gates of physical quantum gates positioned between depth a and depth b of the quantum circuit, information G 2 (a:b) about a number of 2-qubit gates of the physical quantum gates positioned between the depth a and the depth b of the quantum circuit, error information E a (.| i) about a physical quantum gate positioned at the depth a of the quantum circuit, and measurement outcome probability information P̂ b (.| i) about a physical quantum gate positioned at the depth b of the quantum circuit, or b) the deep learning model is a hybrid convolutional neural network (H-CNN) model, the input data of the deep learning model comprises at least one of gate matrix information G' ij indicating a number of gates between qubit i and qubit j of the quantum circuit, first local error information E a L about a physical quantum gate positioned at depth a of the quantum circuit, second local error information E b L about a physical quantum gate positioned at depth b of the quantum circuit, target state information t about the quantum circuit, and target measurement outcome probability information P̂ b ( t | i) about the physical quantum gate positioned at the depth b of the quantum circuit. detecting, by the processor, a quantum circuit to be mitigated among a plurality of quantum circuits, each of the plurality of quantum circuits comprising a combination of a plurality of physical quantum gates; invoking, by the processor, a pre-trained deep learning model for mitigating an error of the detected quantum circuit; inferring, by the processor, an error correction value of the detected quantum circuit by inputting an input data corresponding to the detected quantum circuit to the invoked deep learning model; and mitigating, by the processor, an error of the detected quantum circuit based on the inferred error correction value, wherein, a) the deep learning model is a deep neural network model, the input data of the deep learning model model comprises at least one of information G 1 (a:b) about a number of 1-qubit gates of physical quantum gates positioned between depth a and depth b of the quantum circuit, information G 2 (a:b) about a number of 2-qubit gates of the physical quantum gates positioned between the depth a and the depth b of the quantum circuit, error information E a (.| i) about a physical quantum gate positioned at the depth a of the quantum circuit, and measurement outcome probability information P̂ b (.| i) about a physical quantum gate positioned at the depth b of the quantum circuit, or b) the deep learning model is a hybrid convolutional neural network (H-CNN) model, the input data of the deep learning model comprises at least one of gate matrix information G' ij indicating a number of gates between qubit i and qubit j of the quantum circuit, first local error information E a L about a physical quantum gate positioned at depth a of the quantum circuit, second local error information E b L about a physical quantum gate positioned at depth b of the quantum circuit, target state information t about the quantum circuit, and target measurement outcome probability information P̂ b ( t | i) about the physical quantum gate positioned at the depth b of the quantum circuit. a) the deep learning model is a deep neural network model, the input data of the deep learning model model comprises at least one of information G 1 (a:b) about a number of 1-qubit gates of physical quantum gates positioned between depth a and depth b of the quantum circuit, information G 2 (a:b) about a number of 2-qubit gates of the physical quantum gates positioned between the depth a and the depth b of the quantum circuit, error information E a (.| i) about a physical quantum gate positioned at the depth a of the quantum circuit, and measurement outcome probability information P̂ b (.| i) about a physical quantum gate positioned at the depth b of the quantum circuit, or b) the deep learning model is a hybrid convolutional neural network (H-CNN) model, the input data of the deep learning model comprises at least one of gate matrix information G' ij indicating a number of gates between qubit i and qubit j of the quantum circuit, first local error information E a L about a physical quantum gate positioned at depth a of the quantum circuit, second local error information E b L about a physical quantum gate positioned at depth b of the quantum circuit, target state information t about the quantum circuit, and target measurement outcome probability information P̂ b ( t | i) about the physical quantum gate positioned at the depth b of the quantum circuit.