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
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