Classical vs. Quantum Machine Learning for Breast Cancer Detection
Sonia Díaz-Santos, Daniel Escánez-Expósito · 2023
Breast cancer is one of the top causes of mortality in women throughout the world, and early identification is critical for successful treatment. The accuracy of breast cancer diagnosis has been improved thanks to machine learning. This research compares the effectiveness of conventional and quantum machine learning systems for detecting breast cancer in great detail. Using a publically accessible data set, the project will examine several quantum machine learning models and compare them to classical machine learning algorithms. The results of this study could provide insights into the potential benefits of quantum machine learning for breast cancer detection and ultimately contribute to improving the accuracy of breast cancer diagnosis.