Using machine learning to improve multi-qubit state discrimination of trapped ions from uncertain EMCCD measurements

Abstract

This paper proposes a residual network (ResNet)-based convolutional neural network (CNN) model to improve multi-qubit state measurements using an electron-multiplying charge-coupled device (EMCCD). The CNN model is developed to simultaneously use the intensity of pixel values and the shape of ion images in determining the quantum states of ions. In contrast, conventional methods use only the intensity values. In our experiments, the proposed model achieved a 99.53±0.14% mean individual measurement fidelity (MIMF) of 4 trapped ions, reducing the error by 46% when compared to the MIMF of maximum likelihood estimation method of 99.13±0.08%. In addition, it is experimentally shown that the model is also robust against the ion image drift, which was tested by intentionally shifting the ion images.