Training method and electronic device for quantum machine learning

Abstract

This disclosure proposes a training method for quantum machine learning and an electronic device. The training method includes: configuring a quantum circuit to output probabilities of multiple qubits, where the quantum circuit comprises multiple gates with circuit parameters; mapping the qubits to multiple model parameters of a neural network, where multiple bases are calculated based on the qubits, and the quantity of the bases is greater than or equal to the quantity of the model parameters; inputting data into the neural network and calculating a loss based on the output of the neural network; and updating the circuit parameters in the quantum circuit according to the loss. AbstractAbstract This disclosure proposes a training method for quantum machine learning and an electronic device. The training method includes: configuring a quantum circuit to output probabilities of multiple qubits, where the quantum circuit comprises multiple gates with circuit parameters; mapping the qubits to multiple model parameters of a neural network, where multiple bases are calculated based on the qubits, and the quantity of the bases is greater than or equal to the quantity of the model parameters; inputting data into the neural network and calculating a loss based on the output of the neural network; and updating the circuit parameters in the quantum circuit according to the loss. 20 25 20 13 35 25 F eb 2 02 5 A b s t r a c t 2 0 2 5 2 0 1 3 3 5 2 5 F e b 2 0 2 5 D a ta O u tp u t L o s s U p d a te c ir c u it p a ra m e te rs a n d m o d e l p a ra m e te rs 2 / 4 Update circuit parameters and model parameters 231 210 Data 10> Ry 10> Ry Loss 211 10> Ry 221 230 222 240 220 Output 10> Ry 214 212 213 232 FIG. 2 20 25 20 13 35 25 F eb 2 02 5 2 0 2 5 2 0 1 3 3 5 2 5 F e b 2 0 2 5 Update circuit parameters a n d m o d e l p a r a m e t e r s R y R y R y . . . . . . . . . . . . R yx

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