用CNN区分量子猫态与相干态，准确率达100%
Recognition of Schrodinger cat state based on CNN
- 用LeNet和ResNet处理非线性生成的猫态与相干态数据
- ResNet在测试集上达到100%准确率，优于LeNet的97.5%
- ResNet有效避免传统网络误判，适合量子态识别任务
我们应用卷积神经网络对猫态和相干态进行分类。首先，从非线性过程中生成猫态和相干态的数据集，并进行预处理。随后构建LeNet和ResNet网络结构，调整卷积核、步幅等参数至最优。在训练集上训练两个模型，损失函数显示ResNet在分类性能上更优。最后在测试集上评估，LeNet准确率为97.5%，ResNet达到100%。通过不同α值的猫态与相干态验证了模型一定的泛化能力。结果表明，LeNet可能在缺乏相干特征时将相干态误判为猫态，而ResNet提供了有效解决方案，克服了传统神经网络对两类态的误判问题。
原文摘要 · Abstract (English)
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 α, 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。