Implementation of Quantum Annealing: A Systematic Review

Abstract

Quantum annealing is a quantum computing approach widely used for optimization and probabilistic sampling problems. It is an alternative approach designed due to the limitations of gate-based quantum computing models. The method is observed to have a significant impact on different fields such as machine learning, graphics, routing, scheduling, computational chemistry, computational biology, security, portfolio, and others despite the fact that it is relatively new. This research provides a systematic review of research development trends in the field of quantum annealing and analyzes how it has been implemented in different problem domains. The results are expected to serve as the basis to identify the opportunities and challenges of research related to its implementation. The main contribution of this systematic review is to summarize different implementations of quantum annealing. It is also to analyze the prospect and opportunities in one of the problem domains with the greatest interest which is machine learning.

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“…FIGURE 1. Overview of systematic literature review [13] We highlight the significance of this study by identifying gaps and challenges in the existing literature, emphasizing the need for a comprehensive understanding of current research in the field. The outline of the steps conducted to carry out a systematic review is as follows: 4 and questions Q1-Q5 in the research quality assessment, resulting in 90 articles eligible for analysis and synthesis.…”

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