Quantum Convolutional Neural Network (QCNN) for Image Classification

Development of a quantum convolutional neural network for image classification. This code specializes in binary classification. Different convolution and embedding methods will be used for testing.

Use python main.py to run the benchmark.

Components

1. data.py

   • Prepares a dataset. Currently, it transforms the 28x28 px image to a 16x16 px image in order to fit it into 8 qubits. Therefore, it returns the data in 1-D arrays.
   • The function data_load_and_process loads the dataset, normalizes the images, resizes them, and adjusts the labels for binary classification.
   • The dataset is filtered in the way of having just two possible classes in the dataset, this is done to simplify the model.

2. embedding.py

   • Contains functions for embedding classical data into quantum states.
   • The data_embedding function takes the preprocessed data and embeds it into a quantum state using amplitude encoding.

3. QCNN_circuit.py

   • Defines the quantum convolutional neural network (QCNN) circuit. This file contains the overall architecture of the QCNN.
   • The qcnn function constructs the QCNN circuit and returns the output probabilities.

4. components.py

   • Defines unitary operations and layers used in the QCNN. .

5. training.py

   • Contains the training loop for the QCNN. This file includes functions to train the QCNN using a specified dataset. Corrently used "mse"
   • The circuit_training function trains the QCNN using the Adam optimizer and returns the loss history and trained parameters.

6. utils.py

   • Contains smaller functions used in the code.

References

1. Quantum Convolutional Neural Networks
   Henderson et al.
   arXiv:2108.00661
   Original paper inspiring this implementation

2. PennyLane Quantum Machine Learning
   Xanadu AI
   PennyLane Documentation
   Quantum framework used for circuit construction

3. MNIST Dataset
   Yann LeCun et al.
   Official MNIST Website
   Standard handwritten digit dataset

4. Fashion-MNIST Dataset
   Zalando Research
   GitHub Repository
   Clothing item classification dataset

5. Quantum Computation and Quantum Information
   Michael A. Nielsen & Isaac L. Chuang
   Cambridge University Press (2010)
   The standard textbook on quantum computing fundamentals
   ISBN: 978-1-107-00217-3