QML is a relatively recent and fast-growing field that merges machine learning techniques with quantum computing [1,40]. The idea of combining these two disciplines was first proposed in the early days of quantum computing back in the 1980s [41,42]. In 1985, Berthiaume, and Feynman exploited the concept of a universal quantum computer [43], which laid the foundation for quantum computing [42]. In 1995, some quantum models such as neural networks were proposed [44,45]; in 1994, Shor developed an algorithm [46] that could factor large numbers exponentially faster than any classical algorithm and proved that quantum computers could surpass classical ones for certain tasks. Then, in 1996, Grover’s algorithm was developed, according to which unsorted databases can be searched quadratically faster than classical algorithms [47]. In the early 2000s, researchers focused on understanding how these quantum algorithms could boost machine learning techniques, such as pattern recognition and optimization [48]. In the 2000s, the topic of applying statistical theory to a quantum framework was discussed but received modest attention at that time. Many workshops on quantum computation and learning were organized; in the third event of the proceeding, Bonner and Freivals observed that quantum learning is an emerging theory [49], and its scientific production is rather fragmented. The QBoost algorithm was given by Schuld, and Petruccione and co-workers in 2009 [45], which was performed on the first commercial quantum annealer, ‘the D-Wave device’. The intersection of quantum computing and machine learning was initiated in 2010 [45,50]. Now, researchers have started developing QML algorithms that can exploit the potential of quantum computing for tasks like clustering, classification, and regression [48]. The QML term came into use around 2013. Mohseni, Lloyd, and Rebentrost [51] mentioned the term in their 2013 manuscript. In 2014, Peter Wittek [52] published a monograph with the title ‘QML—What quantum computing means to data mining’; it contains a summary of some previous papers. In the same year, the idea of integrating quantum with machine learning techniques like Support Vector Machines (SVMs) [53] and quantum neural networks (QNNs) [54] was proposed. The aim of these models was to harness quantum entanglement and superposition to represent that in a way that classical neural networks cannot represent. Quantum principal component analysis (QPCA) [55] is a machine learning technique that expresses the potential of quantum algorithms to perform tasks like linear algebra faster than classical algorithms. The present generation of quantum computers are used to perform specific tasks like linear algebra, feature selection, and optimization [56]. The current trend in QML is to focus on the development of ‘hybrid quantum-classical algorithms’ [6,57] so that the systems can combine the quantum and classical tactics. Firms like Google, IBM, and Rigetti are achieving noticeable performance in the development of the quantum processors [58] that support much more complex QML algorithms.