Comparison of different hybrid quantum machine learning approaches for image classification on quantum computers

Comparison of different hybrid quantum machine learning approaches for image classification on quantum computers

Abstract:

Nowadays, Machine learning (ML) and the classification of images are becoming increasingly important. ML is used amongst others in autonomous vehicles to determine obstacles or in medicine for the automatic detection of diseases. However, the demands on neural networks used for image classification are constantly increasing as the features in the images become more and more complex. A promising solution in this area is quantum computing, or more precisely quantum machine learning (QML). Due to the advantages that qubits used in quantum computers bring with them, QML approaches could achieve significantly faster and better results than conventional ML methods. Quantum computing is currently in the so-called ’noisy intermediate-scale quantum’ (NISQ) era which means that quantum computers only have a few qubits, which are prone to errors. Accordingly, quantum machine learning cannot be easily implemented. The solution are hybrid approaches that use classical structures and combine them with quantum circuits.

This work analyzes the hybrid approaches Quanvolutional Neural Network (QCNN), Quantum Transfer Learning (QTL) and Variational Quantum Circuit (VQC). These are trained to classify the images of the MNIST data set. The training is takes place several times with different seeds in order to test the robustness of the approaches. They are then compared based on accuracy, loss and training duration. Additionally, a conventional Convolutional Neural Network (CNN) is used for comparison. Finally, the most efficient approach will be determined. The evaluation of the experiment shows that the QCNN achieves significantly better results than QTL and VQC. However, the conventional CNN performs better than the QCNN in all metrics.

Author:

Nicolas Holeczek

Advisors:

Leo Sünkel, Philipp Altmann, Claudia Linnhoff-Popien

Student Thesis | Published December 2024 | Copyright © QAR-Lab
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