Prediction of Covid-19 and Pneumonia Diseases from Lung X-ray Images Using Quantum Convolutional Neural Networks

Year 2024, Volume: 14 Issue: 2, 37 - 51, 23.07.2024

Seçmen Şahin , Güneş Harman

Abstract

Quantum Convolutional Neural Networks (QCNNs) aim to enhance the capabilities of convolutional neural networks by leveraging the strengths of quantum computing. They operate by locally transforming input data using quantum circuits. In this study, two models have been built on a quantum-encoded COVID-19 dataset. Model-1 classifies between ‘Normal Person’ and ‘Covid19/Viral Pneumonia’, while Model-2 classifies between ‘Covid-19’ and ‘Viral Pneumonia’. Three different classifications have been made based on the number of qubits (feature count) for these models. For Quantum Classifier 1, approximately 70% accuracy was achieved by extracting 11 features from the 256-feature input data obtained through basic data analysis. In Quantum Classifier 2, using the TruncatedSVD method, each image’s 256 features were reduced to 4, resulting in 72% accuracy. Finally, Quantum Classifier 3 achieved an unexpected 76% accuracy using only 2 features. These models provide significant insights into diagnosing diseases from lung X- Ray images and demonstrate how quantum computers can be effectively utilized in the healthcare domain. Additionally, the impact of different parameters in the “default qubit” device of Pennylane on model performance has been investigated. The study highlights how Quantum Classifier 3 achieves high accuracy by significantly reducing the data dimension, indicating the potential of QCNNs to provide high performance with less resource usage in the future.

Keywords

Covid’19, quantum circuit, quantum machine learning, quantum convolutional neural networks

Akciğer Röntgen Görüntülerinden Covid-19 ve Zatürre Hastalığının Kuantum Evrişimli Sinir Ağları Yöntemi ile Tahmini

Abstract

Kuantum Evrişimli Sinir Ağları (QCNN’ler) kuantum hesaplamanın güçlü yönlerinden faydalanarak evrişimli sinir ağlarının yeteneklerini artırmayı amaçlar. Girdi verilerini yerel olarak dönüştürerek ve kuantum devreleri kullanarak çalışırlar. Bu çalışmada, kuantum kodlu bir COVID-19 veri kümesi üzerinde iki model oluşturulmuştur. Model-1, ‘Normal Kişi’ ile ‘Covid19/Viral Pnömoni’ arasında sınıflandırma yaparken, Model-2 ‘Covid-19’ ile ‘Viral Pnömoni’ arasında sınıflandırma yapmaktadır. Oluşturulan bu modeller için kübit sayısına göre (öznitelik sayısı) 3 farklı sınıflandırma yapılmıştır. Kuantum Sınıflandırıcı 1 için, temel veri analizi ile elde edilen 256 özellikli giriş verisinden 11 özellik çıkarılarak yaklaşık %70 doğruluk elde edilmiştir. Kuantum Sınıflandırıcı 2’de, TruncatedSVD yöntemi kullanılarak her bir görüntünün 256 özelliği 4’e indirilmiş ve %72 doğruluk elde edilmiştir. Son olarak Kuantum Sınıflandırıcı 3’te sadece 2 özellik kullanarak beklenmedik bir şekilde %76 doğruluk elde ettiği belirtilmiştir Bu modeller, akciğer röntgen görüntülerinden hastalık teşhisi konusunda önemli bilgiler sağlamakta ve kuantum bilgisayarlarının sağlık alanında nasıl etkili bir şekilde kullanılabileceğini göstermektedir. Ayrıca, Pennylane’in “varsayılan qubit” cihazındaki farklı parametrelerin model performansına etkisi incelenmiştir. Çalışmada, Kuantum Sınıflandırıcı 3’ün veri boyutunu önemli ölçüde azaltarak yüksek doğruluk oranına nasıl ulaştığı, QCNN’lerin gelecekte daha az kaynak kullanımıyla yüksek performans sağlama potansiyelini göstermektedir.

Keywords

Covid’19, kuantum devresi, kuantum makine öğrenmesi, kuantum evrişimli sinir ağları

References

• Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S. 2021. The power of quantum neural networks. Nature Computational Science, 1, 403–409.
• Adhikary, S., Dangwal, S., Bhowmik, D. 2020. Supervised learning with a quantum classifier using multi-level systems. Quantum Information Processing, 19, 89.
• Aïmeur, E., Brassard, G., Gambs, S. 2006. Machine learning in a quantum world. In Conference of the Canadian society for computational studies of intelligence (pp. 431–442). Springer.
• Alvarez-Rodriguez, U., Sanz, M., Lamata, L., Solano, E. 2018. Quantum artificial life in an ibm quantum computer. Scientific Reports, 8, 1–9.
• Amin, M. H., Andriyash, E., Rolfe, J., Kulchytskyy, B., Melko, R. 2018. Quantum boltzmann machine. Physical Review X, 8, Article 021050.
• Bausch, J. 2020. Recurrent quantum neural networks. In Advances in neural information processing systems, 33.
• Beer, K., Bondarenko, D., Farrelly, T., Osborne, TJ., Salzmann, R., Scheiermann, D., et al. 2020. Training deep quantum neural networks. Nature Communications, 11, 1–6.
• Chen, SYC., Yoo, S. 2021. Federated quantum machine learning. Entropy, 23, 460.
• Dang, Y., Jiang, N., Hu, H., Ji, Z., & Zhang, W. 2018. Image classification based on Quantum KNN Algorithm
• Chrisley, R. 1995. Quantum learning. In New directions in cognitive science: Proceedings of the international symposium, Vol. 4. Saariselka: Citeseer.
• Dang, Y., Jiang, N., Hu, H., Ji, Z., Zhang, W. (2018). Image classification based on quantum K-Nearest-Neighbor algorithm. Quantum Information Processing, 17, 1-18.
• da Silva, AJ., Ludermir, TB., de Oliveira, WR. 2016. Quantum perceptron over a field and neural network architecture selection in a quantum computer. Neural Networks, 76, 55–64.
• Dunjko, V., Briegel, HJ. 2018. Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Reports on Progress in Physics, 81, Article 074001
• Dunjko, V., Taylor, JM., Briegel, HJ. 2016. Quantum-enhanced machine learning. Physical Review Letters, 117, Article 130501.
• Ezhov, AA., Ventura, D. 2000. Quantum neural networks. In Future directions for intelligent systems and information sciences, 213–235. Springer
• Gao, Z., Ma, C., Song, D., Liu, Y. 2017. Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis. Neurocomputing, 238, 13–23.
• Henderson, M., Shakya, S., Pradhan, S., Cook, T. 2020. Quanvolutional neural networks: powering image recognition with quantum circuits. Quantum Machine Intelligence, 2, 1–9.
• Huang, HY., Broughton, M., Mohseni, M., Babbush, R., Boixo, S., Neven, H., et al. 2021. Power of data in quantum machine learning. Nature Communications, 12, 1–9.
• Kerenidis, I., Prakash, A. 2017. Quantum gradient descent for linear systems and least squares. arXiv:1704.04992 (quant-ph).
• Khoshaman, A., Vinci, W., Denis, B., Andriyash, E., Amin, MH. 2018. Quantum variational autoencoder. Quantum Science and Technology, 4, Article 014001. arXiv:1802.05779v1 (quant-ph).
• Lamata, L. 2020. Quantum machine learning and quantum biomimetics: A perspective. Machine Learning: Science and Technology, 1, Article 033002.
• Levine, Y., Sharir, O., Cohen, N., Shashua, A. 2019. Quantum entanglement in deep learning architectures. Physical Review Letters, 122, Article 065301.
• Lloyd, S., Mohseni, M., Rebentrost, P. 2013. Quantum algorithms for supervised and unsupervised machine learning. arXiv preprint arXiv:1307.0411.
• Mari, A., Bromley, TR., Izaac, J., Schuld, M., Killoran, N. 2020. Transfer learning in hybrid classical-quantum neural networks. Quantum, 4, 340.
• Mitarai, K., Negoro, M., Kitagawa, M., Fujii, K. 2018. Quantum circuit learning. Physical Review A, 98, Article 032309.
• McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, SC., Yuan, X. 2020. Quantum computational chemistry. Reviews of Modern Physics, 92, Article 015003.
• Nawaz, SJ., Sharma, SK., Wyne, S., Patwary, MN., Asaduzzaman, M. 2019. Quantum machine learning for 6 g communication networks: State-of-the-art and vision for the future. IEEE Access, 7, 46317–46350.
• Pepper, A., Tischler, N., Pryde, GJ. 2019. Experimental realization of a quantum autoencoder: The compression of qutrits via machine learning. Physical Review Letters, 122, Article 060501.
• Pomarico, D., Fanizzi, A., Amoroso, N., Bellotti, R., Biafora, A., Bove, S., et al. 2021. A proposal of quantum-inspired machine learning for medical purposes: An application case. Mathematics, 9, 410.
• Rahman M., Geiger, D. 2016. Quantum clustering and gaussian mixtures. arXiv:1612.09199v1 (stat.ML).
• Rebentrost, P., Mohseni, M., Lloyd, S. 2014. Quantum support vector machine for big data classification. Physical Review Letters, 113, Article 130503.
• Rieffel, EG., Venturelli, D., O’Gorman, B., Do, MB., Prystay, EM., Smelyanskiy, VN. 2015. A case study in programming a quantum annealer for hard operational planning problems. Quantum Information Processing, 14, 1–36.
• Schuld, M., Sinayskiy, I. ve Petruccione, F. 2015. An introduction to quantum machine learning. Contemporary Physics, 56(2), 172-185.
• Schuld, M., Sinayskiy, I., Petruccione, F. 2015. Simulating a perceptron on a quantum computer. Physics Letters. A, 379, 660–663.
• Schuld, M., Sinayskiy, I., Petruccione, F. 2016. Prediction by linear regression on a quantum computer. Physical Review A, 94, Article 022342.
• Schuld, M. 2018. Supervised learning with quantum computers. Springer.
• Sheng, YB., Zhou, L. 2017. Distributed secure quantum machine learning. Science Bulletin, 62, 1025–1029.
• Von Lilienfeld, OA. 2018. Quantum machine learning in chemical compound space. Angewandte Chemie International Edition, 57, 4164–4169.
• Wallnöfer, J., Melnikov, AA., Dür, W., Briegel, HJ. 2020. Machine learning for long-distance quantum communication. PRX Quantum, 1, Article 010301.
• Zhou, R. 2010. Quantum competitive neural network. International Journal of Theoeretical Physics, 49, 110–119.
• Zhong, Y., Yuan, C. 2012. Quantum competition network model based on quantum entanglement. Journal of Computers, 7, 2312–2317.
• Zidan, M., Abdel-Aty, AH., El-shafei, M., Feraig, M., Al-Sbou, Y., Eleuch, H., et al. 2019. Quantum classification algorithm based on competitive learning neural network and entanglement measure. Applied Sciences, 9, 1277.