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All Journal Algoritme Jurnal Mahasiswa Teknik Informatika
Putri Naomi Febrianti Telaumbanua
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Computer Science & IT

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Analisis Perbandingan Performa Hybrid Quantum-Classical Neural Network dan Convolutional Neural Network dalam Klasifikasi Citra Putri Naomi Febrianti Telaumbanua; Sunneng Sandino Berutu; Aninda Astuti
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 7 No 1 (2026): October 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v7i1.15735

Abstract

The primary challenge in employing conventional CNNs for medical image classification lies in their high parameter complexity and the potential degradation in generalization efficiency on large-scale datasets. This study explores an alternative solution through a hybrid architecture, namely the HQCNN, applied to the PathMNIST dataset. In this schema, classical CNN layers function as a spatial feature extractor, which is then mapped into the quantum domain via angle encoding mechanisms on a 4-qubit configuration. The novelty of this research lies in the scalability testing of the model, conducted incrementally up to a volume of 130,000 data samples. Experimental results confirm the superiority of the HQCNN, achieving an accuracy of 83.0%, significantly outperforming the classical CNN model, which exhibited a decline in effectiveness at 77.7%. These findings indicate that the integration of quantum principles, such as entanglement via CNOT gates, enables sharper class separation and reduces the risk of overfitting in large scale medical datasets.
Co-Authors Aninda Astuti Sunneng Sandino Berutu