Investigating the potential of Quantum Inspired Machine Learning approaches for mental health detection of young adults

Abstract

Medical Health Informatics has envisaged quantum computing as flurry of promising solutions to deal with mental health related issues of young adults in recent times. Even though traditional machine algorithms try to find an amicable solution to healthcare service providers, still the efficient detection and classification of the highly complex patterns that emerges from the healthcare datasets needs further investigations. Quantum machine learning, inspired from the principle from Quantum computing is found to be a transformative alternative in redefining the healthcare applications. In this article, Quantum inspired Support Vector Machines (QISVMs) and Quantum inspired Neural Networks (QINN) as two efficient quantum machine learning approaches are proposed by combining the traditional yet very popular and powerful machine learning algorithms such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) with the power of quantum computing principles. The proposed Quantum inspired machine learning approaches are evaluated with several figure of merit and then, compared with the performance of the traditional algorithms counterpart for analyzing the most complex nature of the healthcare data with proper identification of minute wise data patterns and biomarkers in order to detect the early-stage healthcare issues pertained to a patient. Experimental study shows the efficacy of these quantum machine learning methods in comparison to the existing literature with threats to validity, some hidden challenges, and ethical issues associated with these technological advancements.