Recognition of Schrodinger cat state based on CNN

TL;DR

This paper demonstrates that deep convolutional neural networks, especially ResNet, can accurately classify Schrödinger cat states and coherent states, showing high accuracy and generalization in quantum state recognition.

Contribution

The study applies CNN architectures to quantum state classification, optimizing parameters and showing ResNet's superior performance over LeNet in recognizing Schrödinger cat states.

Findings

ResNet achieved 100% accuracy on test data.

ResNet outperformed LeNet in classification accuracy.

The models demonstrated generalization across different state parameters.

Abstract

We applied convolutional neural networks to the classification of cat states and coherent states. Initially, we generated datasets of Schrodinger cat states and coherent states from nonlinear processes and preprocessed these datasets. Subsequently, we constructed both LeNet and ResNet network architectures, adjusting parameters such as convolution kernels and strides to optimal values. We then trained both LeNet and ResNet on the training sets. The loss function values indicated that ResNet performs better in classifying cat states and coherent states. Finally, we evaluated the trained models on the test sets, achieving an accuracy of 97.5% for LeNet and 100% for ResNet. We evaluated cat states and coherent states with different {\alpha}, demonstrating a certain degree of generalization capability. The results show that LeNet may mistakenly recognize coherent states as cat states without coherent features, while ResNet provides a feasible solution to the problem of mistakenly recognizing cat states and coherent states by traditional neural networks.