ICCAD Special Session Paper: Quantum-Classical Hybrid Machine Learning for Image Classification

by   Mahabubul Alam, et al.

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. On one hand, QML models can be useful in both of these tasks. Convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoencoder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.


Quantum machine learning for image classification

Image recognition and classification are fundamental tasks with diverse ...

Variational Quanvolutional Neural Networks with enhanced image encoding

Image classification is an important task in various machine learning ap...

Hybrid quantum-classical convolutional neural network for phytoplankton classification

The taxonomic composition and abundance of phytoplankton, having direct ...

Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack

Image classification must work for autonomous vehicles (AV) operating on...

Efficient Quantum Feature Extraction for CNN-based Learning

Recent work has begun to explore the potential of parametrized quantum c...

Quantum Enhanced Filter: QFilter

Convolutional Neural Networks (CNN) are used mainly to treat problems wi...

Application of Quantum Density Matrix in Classical Question Answering and Classical Image Classification

Quantum density matrix represents all the information of the entire quan...

Please sign up or login with your details

Forgot password? Click here to reset