Application of Quantum Convolutional Neural Networks for Breast Cancer Detection

Machine learning learns and obtains a whole idea from a given input-output relation to produce a desired result. Image classification, speech recognition, clustering, and classification have become important parts of major IT industries. Nowadays, IT industries are developing algorithms and techniques to further improve results. The idea of combining quantum computing with any part of recent computational theory is going to change the time and space complexity of many algorithms. In recent times, the emerging field of quantum computing has given an idea regarding whether it is possible to use quantum computing in machine learning to improve classical machine learning algorithms. This paper focuses on elaborating the concept behind Quantum Convolutional Neural Networks, discussing their architecture and their efficiency against classical Convolutional Neural Networks. After thoroughly understanding the techniques of quantum computing and applying the same to supervised learning in the quantum framework, it is possible to enhance the application of Quantum Convolutional Neural Networks in real life. The Quantum Convolutional Neural Network will be applied to detect breast cancer in the dataset. The particular steps and the process of minimizing loss will be discussed to achieve higher accuracy of the model by applying the convolutional neural network model with quantum features of the quantum framework. Keywords - Quantum Machine Learning, Quantum computing, Quantum Convolutional Neural Network, TensorFlow