This is a Python project simulating a Quantum-classical hybrid convolutional neural network(QC-CNN) for classical image classification. A Chinese version of detailed code analysis can be found here.

Directory structure:

|—— run.py
|—— app
|     |—— load_data.py
|     |__ train.py
|—— model
|     |—— calssical.py  // a classical CNN with one concolutional layer
|     |—— single_encoding.py  // a quantum-classical hybrid model using single encoding method
|     |—— multi_encoding.py  // a hybrid model using multiple encoding method
|     |—— inception.py  // this model contains a quantum-classical hybrid inception module
|     |__ multi_noisy.py  // same model as multi_encoding.py but simulating mixed states
|—— datasets
      |__ // csv datasets files

• Run run.py.
• Edit console printing outputs in train.py.
• Network structures are defined in the files in model directory. Select the model to be used in classification by importing it in run.py. The default model is a classical CNN.
• The default datasets files in datasets are csv format data of MNIST and FashionMNIST images subsampled to $14 \times 14$ size. Each dataset contains 3 types of images, 400 in each type, for example fashion_012_1200.csv means totally 1200 images with type 0,1,2 in FashionMNIST. The data loader for local datasets is load_data.py in app.

Due to the upgration of PennyLane, models using multi-encoding method (multi_encoding.py & multi_noisy.py) cannot run under latest version of PennyLane. To run these two models, please use PennyLane v0.23.0, and downgrade autoray to 0.2.5:

pip uninstall autoray
pip install autoray==0.2.5

If you are using MacOS, install jax with conda install jax -c conda-forge.

This problem is due to this line of code:

exec('qml.{}({}, wires = {})'.format(encoding_gates[i], inputs[qub * var_per_qubit + i], qub))

For now I'm not fixing this problem since I'm not actively working on PennyLane, if you are familiar with latest version of PennyLane you are very welcomed to commit to this project :)

A QC-CNN is constructed with both classical neural network layers and quantum circuits. The quantum circuits are also named as quantum convolution kernels in some papers. In this project, data is firstly fed into a quantum circuit, the output feature maps are then fed into fully connected layers and then give the classification result. You can freely build your own model and put it in model without changing other parts of the project.

A typical quantum convolution kernel contains an encoding module, to encode classical data into quantum states, and a trainable entangling module, to extract features from data. The following picture shows the quantum convolution kernel in single_encoding.py. It uses single encoding method, encoding one classical data $x_i$ into one qubit.

When encoding multiple data into one qubit, it is called mutiple encoding method, the multiple encoding module in multi_encoding.py is:

Note that the kernel size is correspondingly changed when using different encoding methods.

Inception module is a sturcture with parallel convolution kernels proposed in GoogLeNet. What if building one with quantum convolution kernels? The hybrid Inception module in inception.py is:

Among all the models in model, this one has the best performance :P.