Quantum machine learning model training method and device based on block coding

Abstract

The invention relates to the technical field of quantum machine learning, in particular to a quantum machine learning model training method and device based on block coding, and the method comprises the steps: obtaining input data and a loss function of a target layer in a to-be-trained quantum machine learning model; coding a transpose matrix of the input data and a target first gradient block to the quantum circuit to obtain a first quantum circuit; the target first gradient is a gradient of an output data relative loss function obtained by the input data passing through a target layer; operating the first quantum circuit to obtain a second gradient of the parameter of the parameter-containing non-unitary matrix relative to the loss function; and updating the parameter of the parameter-containing non-unitary matrix according to the second gradient so as to update the value of the loss function and the target first gradient until the loss function reaches a training cut-off condition. According to the method, more functions of the classic neural network can be fitted and simulated, the loss function of the classic neural network can be optimized more easily by combining the parallel computing advantage of quantum computing, the parameter gradient in a quantum machine learning model can be calculated conveniently, and the number of quantum line operation times is greatly reduced.