Quantum Machine Learning for the Future Medical Analysis: A Comprehensive Review and Future Prospects
Abstract
The combination of Quantum Computing with Machine Learning approaches is refered as Quantum Machine Learning (QML). Recently, there have been significant advancements in performance and computational speed to quantum computing techniques. Although traditional algorithms have shown promising outcomes, their low processing efficiency and limited labelled data are common problems. To address these issues, a great deal of research has brought machine learning and quantum computing together, which has led to the development of increasingly complex algorithms. Notable gains in parameter optimisation, execution efficiency, and error rate reduction have resulted from these developments. The incorporation of quantum machine learning has yielded novel perspectives into the transdisciplinary domain of quantum technology and medical image analysis, indicating noteworthy advancements to be made in this area going forward. QML is becoming known for its unique superposition and entanglement capabilities, which make it a cutting-edge approach to challenges involving categorization. Its uses in fields such as pattern identification, password cracking, and medical image analysis have attracted a lot of attention. Because of this, a growing number of academics are using QML-based algorithms to analyse medical data. This research review's key goal is to present an in-depth analysis of how QML techniques are used and involved in the medical field. This article includes a synopsis of the…
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