Quantum Algorithms for Deep Convolutional Neural Networks

Description

This repository contains an implementation of the quantum convolutional layer and its application to the MNIST classification task in :

• Paper : Quantum Algorithms for Deep Convolutional Neural Networks
• Authors : Kerenidis, Landman and Prakash
• Date : 2019

Setup

To install the quantum convolutional layer, clone this repository and execute the following commands :

$ cd qml-qcnn
$ pip install -e .

Details

The quantum convolutional layer is a PyTorch module with the following parameters :

• in_channels (int): number of input channels.
• out_channels (int): number of output channels.
• kernel_size (int): size of the convolution kernel.
• eps (float): precision of quantum multiplication.
• cap (float): value for cap 'relu' activation function.
• ratio (float): precision of quantum tomography.
• delta (float): precision of quantum gradient estimation.
• stride (int, optional): convolution stride. Defaults to 1.
• padding (int, optional): convolution padding. Defaults to 0.
• dilation (int, optional): convolution dilation. Defaults to 1.

The bias is unsupported for now.

Example

In Pytorch, you can use the 2D convolution module as follows :

from torch import nn

torch_conv2d = nn.Conv2d(16, 32, 3, stride=1)

Similarly, you can use the 2D quantum convolution module as follows :

import qcnn

eps = 0.01
cap = 10.
ratio = 0.5
delta = 0.01

quantum_conv2d = qcnn.QuantumConv2d(16, 32, 3, eps, cap, ratio, delta, stride=1)

Experiments

The experiments in the paper are reproduced for MNIST classification task using PyTorch Lightning.

• Option 1 : Open in Colab. You can activate the GPU in Notebook Settings.
• Option 2 : Run on local machine. First, you need to install PyTorch Lightning :

$ pip install pytorch_lightning

You can run an experiment using the following command :

$ cd qml-qcnn/scripts/
$ python mnist_classification --eps=0.01 --cap=10 --ratio=0.5 --delta=0.01

Acknowledgements

• Official implementation