Application of Variational Quantum Algorithms in Quantum Machine Learning

Quantum computing, in the last few years, has made tremendous gains both scientifically and commercially. The recent announcements by Google and Microsoft on new qubit implementation approaches, addressing scaling and error correction, forecast the superior capability of quantum computing over classical computing in various application domains. One area that has seen significant advancements in research is the application of quantum algorithms in machine learning, giving rise to the exciting field of quantum machine learning. Therefore, the essence of this paper is to survey the recent advancements in quantum machine learning. The survey primarily focuses on hybrid quantum machine learning, with a particular emphasis on recent research advancements in the application of variational quantum algorithms and data encoding for quantum machine learning. The paper discusses the use of variational quantum algorithms in classification and regression tasks. In classification, recent work in image, speech, and text classification is discussed, highlighting the advances and limitations. In the case of regression, recent research work in single and multi-dimensional regressions is discussed. This survey aims to cover the application of variational quantum algorithms in enhancing the capabilities of hybrid quantum machine learning.