Abstract
AbstractDue to the primary advantage of reduced time complexity, quantum machine learning (QML) is becoming so popular nowadays. Another technique that integrates the concepts of both quantum information theory and machine learning is termed as quantum-inspired machine learning (QML). The QML does not make use of quantum computers and takes the advantage of articulate nature of the quantum theory to improve the accuracy in place of minimizing the time complexity of the process. The work focuses on applying the quantum information theory with the binary classifier to discriminate the severities of breast cancer. For this, the Wisconsin breast cancer diagnosis (WDBC) dataset is employed. As a result, it is revealed that the quantum-inspired classification approach outperformed the traditional binary machine learning algorithms for breast cancer problems.KeywordsQuantumBreast cancerMachine learningWDBCMalignantBenign
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
