A quantization method implemented by a computer to execute a process comprising: calculating, for each of a plurality of layers of a neural network, a difference between a trust region radius threshold, based on a trust region radius, and a quantization error of a first bit width, the quantization error of the first bit width being the quantization error of quantizing a parameter of the neural network with the first bit width, the first bit width being narrower by one step than a second bit width among a plurality of bit width candidates, a quantization error of the second bit width being less than or equal to the trust region radius threshold, the quantization error of the second bit width being the quantization error of quantizing the parameter of the neural network with the second bit width, and the second bit width being the smallest bit width with which the quantization error is less than or equal to the trust region radius threshold; calculating, based on the differences, corresponding scaling coefficients for each of the plurality of layers; updating, for each of the plurality of layers, the trust region radius by using a smallest value among the scaling coefficients, and updating the trust region radius threshold based on the updated trust region radius; and for each of the plurality of layers, quantizing and outputting the parameter of the neural network: by the first bit width, when the quantization error of the first bit width is less than or equal to the updated trust region radius threshold; and by the second bit width, when the quantization error of the first bit width is greater than the updated trust region radius threshold. calculating, for each of a plurality of layers of a neural network, a difference between a trust region radius threshold, based on a trust region radius, and a quantization error of a first bit width, the quantization error of the first bit width being the quantization error of quantizing a parameter of the neural network with the first bit width, the first bit width being narrower by one step than a second bit width among a plurality of bit width candidates, a quantization error of the second bit width being less than or equal to the trust region radius threshold, the quantization error of the second bit width being the quantization error of quantizing the parameter of the neural network with the second bit width, and the second bit width being the smallest bit width with which the quantization error is less than or equal to the trust region radius threshold; calculating, based on the differences, corresponding scaling coefficients for each of the plurality of layers; updating, for each of the plurality of layers, the trust region radius by using a smallest value among the scaling coefficients, and updating the trust region radius threshold based on the updated trust region radius; and for each of the plurality of layers, quantizing and outputting the parameter of the neural network: by the first bit width, when the quantization error of the first bit width is less than or equal to the updated trust region radius threshold; and by the second bit width, when the quantization error of the first bit width is greater than the updated trust region radius threshold. by the first bit width, when the quantization error of the first bit width is less than or equal to the updated trust region radius threshold; and by the second bit width, when the quantization error of the first bit width is greater than the updated trust region radius threshold.