COMPARISON OF QUANTUM DEEP LEARNING METHODS FOR IMAGE CLASSIFICATION

Abstract

Nowadays, with the discovery of the power and potential of quantum computers, developing and understanding quantum-based deep learning models has become an important research area. This study investigates Quantum Transfer Learning and Quantum Hybrid Learning models that involve feature extraction and training processes using Convolutional Neural Networks (CNN) and Vision Transformer (ViT). The study aims to explore the potential advantages and differences of quantum deep learning techniques. It is envisioned that quantum computing can provide significant advantages in terms of computational speed and efficiency, especially in complex and large-scale data sets. Therefore, this study will contribute to a better understanding of the practical applications and potential impacts of quantum deep learning techniques. In this study, we evaluate the performance of four different quantum deep learning architectures using two different datasets. The classifiers used are the pre-trained ResNet-50 with a kernel size of 5x5 and the state-of-the-art CaiT-24-XXS-224 (CaiT) transducers. Optimization was performed with Adam optimizer using the cross entropy loss function. A total of eight models were trained, each with ten iterations. Accuracy (Acc), balanced accuracy (BA), overall F𝛽 (F_beta) macro score F1 and F2, Matthew's Correlation Coefficient (MCC), sensitivity (Sens) and specificity (Spec) were used as performance measures.

Keywords

• Quantum Transfer Learning
• Quantum Artificial Intelligence Models
• Hybrid Quantum-Classical Learning
• Vision Transformers

GÖRÜNTÜ SINIFLANDIRMADA KUANTUM DERİN ÖĞRENME YÖNTEMLERİNİN KARŞILAŞTIRILMASI

Abstract

Günümüzde kuantum bilgisayarların gücü ve potansiyelinin keşfedilmesiyle birlikte, kuantum tabanlı derin öğrenme modelleri geliştirmek ve anlamak önemli bir araştırma alanı haline gelmiştir. Bu çalışma, Evrişimli Sinir Ağları (CNN) ve Vision Transformer (ViT) kullanılarak öznitelik çıkarımı ve eğitim süreçlerini içeren Kuantum Transfer Öğrenme ve Kuantum Hibrit Öğrenme modellerini incelemektedir. Çalışma, kuantum derin öğrenme tekniklerinin potansiyel avantajlarını ve farklılıklarını araştırmayı amaçlamaktadır. Kuantum hesaplamanın, özellikle karmaşık ve büyük ölçekli veri setlerinde hesaplama hızı ve verimlilik açısından önemli avantajlar sağlayabileceği öngörülmektedir. Dolayısıyla, bu çalışma, kuantum derin öğrenme tekniklerinin pratik uygulamalarının ve potansiyel etkilerinin daha iyi anlaşılmasına katkıda bulunacaktır. Bu çalışmada, iki farklı veri seti kullanılarak dört farklı kuantum derin öğrenme mimarisinin performansı değerlendirilmiştir. Kullanılan sınıflandırıcılar, önceden eğitilmiş 5x5 çekirdek boyutuna sahip ResNet-50 ve son teknoloji ürünü CaiT-24-XXS-224 (CaiT) dönüştürücüleridir. Optimizasyon, Adam optimizer ile çapraz entropi kayıp fonksiyonu kullanılarak gerçekleştirilmiştir. Her biri on tekrarlı olmak üzere toplam sekiz model eğitimi yapılmıştır. Performans ölçütleri olarak doğruluk (Acc), dengeli doğruluk (BA), genel F𝛽 makro skorundan F1 ve F2, Matthew's Korelasyon Katsayısı (MCC), duyarlılık (Sens) ve özgüllük (Spec) kullanılmıştır.

Keywords

• Kuantum Transfer Öğrenme
• Kuantum Yapay Zeka Modelleri
• Hibrit Kuantum-Klasik Öğrenme
• Vision Transformers

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