SQNN-ASOC(A Shallow Hybrid Classical-Quantum Spiking Feedforward Neural Network for Noise-Robust Image Classification)

The proposed hybrid classical-quantum SQNN model consists of a classical pre-trained SNN, one dressed quantum layer for classification (whose qubits are further measured to collapse to classical bits) and a final classical post-processing layer for obtaining the outcomes. Additionally, a classical pre-processing layer is employed to connect the temporal pooling layer and the dressed quantum layer of the model architecture. The brief descriptions of different modules used in the development of SQNN are provided below.

1. ClassicalSNNGenerator.ipynb It contains the codes to generate Spiking Neural Networks. This file is used to make the pre-trained network of the proposed SQNN model for the different datasets. It is a classical Sequential model, which consists of the following: (a). Linear Layer: It comprises 784 input features, which are the pixels of the input images (dimension 28 ×28) and 128 output features. (b).Spiking Activation Layer: It generates a certain number of spikes per simulated second corresponding to the prediction of the input features by the Spik- ingReLU activation function. SpikingReLU is chosen because it is the best fit for a Spiking Activation function, unlike sigmoid or glorot functions. This layer adds a temporal dimension to our model. This is where the energy efficiency of SNNs becomes important; if the spikes are generated very rapidly, then the prediction might not be propagated to the further layer and if the spikes are fired for long, then it would be a waste of energy. ( c ) Temporal Average Pooling layer: This layer takes the time average of the spikes generated by the preceding layer and propagates this to the Dressed Quantum Net.
2. [5-ary]_pretrain_5x2_squeeze_MNIST.ipynb It is used to train the proposed SQNN model for the 5-class image datasets. The classical pre- trained model is loaded from the .h5 file in the variable called spikeaware_model and then the class DressedQuantumNet defined. This class defines a layer to wrap a variational quantum layer with a pre and a post network, both classi- cal and Linear. Before adding this layer to out pre-trained model, we removed the last layer of the model loaded in variable spikeaware_model by the following line of code: model_hybrid = copy.deepcopy(torch.nn.Sequential(∗(list (spikeaware_model.children())[: −1]))) Next, the trainable parameters are frozen by using the snippet: for parameter in model_hybrid.parameters() : param.requires_grad = False and the final classi- fication layer is added by the following line of code: model_hybrid.fc = DressedQuantumNet() the labels are modified by : train_labels− = 5 to make them compatible with the modified structure.
3. [5-ary]_pretrain_5x2_squeeze_FashionMNIST.ipynb It contains the codes to train the hybrid classical- quantum networks (5 qubits ansatz) with the given KMNIST dataset. It works in the same way as the [5-ary]_pretrain_5x2_squeeze_MNIST.ipynb notebook, but for FashionMNIST dataset.
4. [5-ary]_pretrain_5x2_squeeze_KMNIST.ipynb It comprises the codes to train the hybrid classical- quantum network (5 qubits ansatz) with KMNIST dataset. It works in the same way as the [5-ary]_pretrain_5x2_squeeze_MNIST.ipynb notebook, but for the KMNIST dataset.
5. [5-ary]_pretrain_5x2_squeeze_INET.ipynb It comprises the codes to train the hybrid classical- quantum network (5 qubits ansatz) with ImageNet dataset. It works in the same way as the [5-ary]_pretrain_5x2_squeeze_MNIST.ipynb notebook, but for the ImageNet dataset.
6. [5-ary]_pretrain_5x2_squeeze_CIFAR.ipynb It comprises the codes to train the hybrid classical- quantum network (5 qubits ansatz) with CIFAR-10 dataset. It works in the same way as the [5-ary]_pretrain_5x2_squeeze_MNIST.ipynb notebook, but for the CIFAR-10 dataset.
7. data_create_FASHIONMNIST.ipynb It comprises codes to create noisy version of Fashion- MNIST dataset. It converts the 7200 test images to their noise affected versions. Various types of noises are added in various intensities and are arranged in an order compatible with the proposed SQNN model.
8. data_create_KMNIST.ipynb It contains codes to create noisy version of KMNIST dataset. Its function is similar to the jupyter notebook named data_create_FASHIONMNIST.ipynb.
9. data_create_MNIST.ipynb It comprises codes to create noisy version of MNIST dataset.Its function is similar to the jupyter notebook named data_create_FASHIONMNIST.ipynb.
10. data_create_CIFAR.ipynb It comprises codes to create noisy version of CIFAR-10 dataset.Its function is similar to the jupyter notebook named data_create_FASHIONMNIST.ipynb.
11. data_create_INET.ipynb It comprises codes to create noisy version of ImageNet dataset.Its function is similar to the jupyter notebook named data_create_FASHIONMNIST.ipynb.
12. Testbench_Fashion_5_class.ipynb It contains the codes for testing networks against noisy version of 5-class Fashion MNIST dataset. It is an or- ganised form of dealing with the test data and useful for plotting and other datapoints.
13. Testbench_KMNIST_5_class.ipynb It includes the codes for testing networks on noisy ver- sion of 5-class KMNIST dataset.
14. Testbench_MNIST_5_class.ipynb This section of the codes is used for testing networks on noisy version of 5-class MNIST dataset.
15. Testbench_CIFAR_5_class.ipynb This section of the codes is used for testing networks on noisy version of 5-class CIFAR-10 dataset.
16. Testbench_INET_5_class.ipynb This section of the codes is used for testing networks on noisy version of 5-class ImageNet dataset.