Emerging Paradigm of Quantum Machine Learning: Knowledge Insights and Future Prospects

The machines can handle tasks that require thinking and decisive capacity by using Artificial Intelligence. But these systems require higher resources for their operations. As such, Quantum Machine Learning (QML) has been explored as a parallel solution for such problems. In our study, we comprehensively analyzed the research evolution within the QML domain. We integrated literature from Scopus and Web of Science to investigate underlying relationships through bibliometric information. The publication and citation trends, country collaboration, keyword co-occurrence, and thematic relationships helped to introspect the growth of QML. Our results show that quantum kernels, quantum natural language processing, contrastive learning, quantum reservoir computing, and explainable QML are gaining attention within academia. It has been expanding across a diverse set of problem spaces like management of resources, intrusion detection, process control, cloud computing, molecular and materials modeling, simulation of integrated circuits, and nuclear physics, etc. The findings also highlight the existing challenges that are limiting the practical implementation of solutions within the NISQ era. In response, we also identified actionable directives that can be followed for advancing the current state of research. These insights will provide future researchers with a single-point reference for guiding their exploration of QML.