Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum err...Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold decoder.In this work,we build a convolutional neural network(CNN)decoder to correct errors in the toric code based on the system research of machine learning.We analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running time.It is shortened by 30%-40%,and we finally design an optimized algorithm for CNN decoder.In this way,the threshold accuracy of the neural network decoder is made to reach 10.8%,which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%-10.3%has been slightly improved,and there is no need to verify the basic noise.展开更多
Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the...Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the specific patterns of image damage,resulting in artifacts and semantic information confusion in repaired images.To address the problems of artifacts and semantic information confusion,inspired by different sensitivities of different RFs to inpainting the same image damaged patterns,this study proposes an image inpainting method based on multiple receptive fields(MRFs)and dynamic matching of damaged patterns.First,the parallel filter banks are used to extract the MRF feature groups.Second,the features are dynamically weighted and screened,guided by the mask image,to construct a relationship that adaptively matches the most relevant RF to each specific damaged pattern.A fast Fourier convolution based decoder is used to enhance the fusion of global contextual features during the reconstruction of high dimensional features into low dimensional images.Comparative experimental results show that the proposed method achieves better subjective and objective inpainting results on three public datasets:Paris StreetView,CelebA-HQ,and Places2.展开更多
基金the National Natural Science Foundation of China(Grant Nos.11975132 and 61772295)the Natural Science Foundation of Shandong Province,China(Grant No.ZR2019YQ01)the Project of Shandong Province Higher Educational Science and Technology Program,China(Grant No.J18KZ012).
文摘Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold decoder.In this work,we build a convolutional neural network(CNN)decoder to correct errors in the toric code based on the system research of machine learning.We analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running time.It is shortened by 30%-40%,and we finally design an optimized algorithm for CNN decoder.In this way,the threshold accuracy of the neural network decoder is made to reach 10.8%,which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%-10.3%has been slightly improved,and there is no need to verify the basic noise.
基金The National Natural Science Foundation of China(No.62261032)the Central Government Guiding Funds for Local Scienceand Technology Development Program(No.25ZYJA026).
文摘Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the specific patterns of image damage,resulting in artifacts and semantic information confusion in repaired images.To address the problems of artifacts and semantic information confusion,inspired by different sensitivities of different RFs to inpainting the same image damaged patterns,this study proposes an image inpainting method based on multiple receptive fields(MRFs)and dynamic matching of damaged patterns.First,the parallel filter banks are used to extract the MRF feature groups.Second,the features are dynamically weighted and screened,guided by the mask image,to construct a relationship that adaptively matches the most relevant RF to each specific damaged pattern.A fast Fourier convolution based decoder is used to enhance the fusion of global contextual features during the reconstruction of high dimensional features into low dimensional images.Comparative experimental results show that the proposed method achieves better subjective and objective inpainting results on three public datasets:Paris StreetView,CelebA-HQ,and Places2.
