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.
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.
Combinatorial optimization is a foundation of numerous scientific, industrial, and social decision-making questions, but its usefulness is mostly restricted by the exponential complexity of the problem of classical computability. The quantum annealing (QA) has become one of the leading analogue quantum systems to address such problems, whereby optimization problems are converted into physical energy structures and quantum fluctuations are utilized to perform systematic exploration. It provides a critical, synthesized, and comprehensive review of quantum annealing as applied to combinatorial optimization, including the theoretical basis, hardware designs, algorithm methods, issues of embedding and encoding, benchmarking procedures, applications, and integration with quantum algorithms based on gates and solvers on a classical computer. We build a comprehensive taxonomy between adiabatic dynamics, Ising and QUBO models, stoquastic and non-stoquastic Hamiltonians, and diabatic transitions to state-of-the-art flux-qubit annealers, new architectures, and hybrid quantum classical pipelines. We show that the overhead of embedding and encoding and the benchmarking approach taken are the most significant factors in determining scalability and performance, but not the raw number of qubits. Transportation, energy systems, robotics, finance, pharmacological discovery, and machine learning are just some examples of the domain-specific case studies that use QA with minimal empirical utility as a hybrid refinement engine and not an independent solver. Moreover, we introduce a stringent evaluation of the existing benchmarking culture, clarify the causal links between QA and QAOA as well as between VQE and structural predicaments, and reveal the institutional roots of the existing restrictions. We conclude by identifying a research roadmap in the future, focusing on the annealing hardness characterization, Stoquastic control manipulation, embedding rules automation, architecture design, and development of principled quantum advantage. It creates a benchmark reference to those in the field and research and deployment of scalable, reliable, and application-relevant quantum optimization
Quantum annealing has emerged as a transformative approach in solving combinatorial optimization problems by leveraging the principles of quantum mechanics. A comprehensive survey of quantum annealing is presented, delineating its foundational concepts, state-of-the-art advancements, and diverse applications across domains such as logistics, finance, machine learning, and energy optimization. Notable contributions include the introduction of a systematic benchmarking framework to evaluate quantum annealing against classical optimization techniques across metrics like time-to-target, scalability, and energy efficiency. Additionally, novel hybrid quantum-classical paradigms and hardware advancements are explored, addressing challenges in qubit connectivity, coherence, and noise resilience. Optimization problems are mapped to suitable quantum annealing platforms, offering practical guidelines for leveraging quantum technologies in real-world scenarios. Critical gaps in scalability and integration with machine learning are identified, with future research directions proposed to bridge these challenges. These findings provide a holistic understanding of quantum annealing's potential, offering a foundational resource for advancing quantum computing in optimization.
Abstract Traffic optimization remains a significant challenge in urban planning and transportation management. While efficient traffic optimization is crucial for enhancing urban mobility, reducing congestion, and promoting environmental sustainability, traditional computational methods often struggle with the complex, dynamic nature of traffic systems. Recent advances in quantum computing, particularly quantum annealing, offer promising new techniques that could revolutionize traffic flow optimization. This work systematically reviews the literature, starting with search term formulation and ending with the final set of articles. These articles are categorized into three groups: (1) traffic signal control, (2) traffic flow optimization, and (3) routing problems optimization (including vehicle routing problem and traveling salesman problem). The review critically examines current studies on quantum annealing-based traffic optimization, focusing on contributions, methods, solvers, problem suitability, key findings, benchmark fairness, and limitations. It identifies key challenges and provides recommendations for future research. Insights from this work offer researchers and practitioners a concise overview of current challenges and future directions in traffic optimization.
Quantum computing is a high-powered computational model that possess the capability to solve problems that are non-detectable by traditional algorithms. These algorithms primarily exploit well-known rules and principles of quantum mechanics i.e., superposition and entanglement to implement and deploy solutions for sophisticated tasks. Its applications have revolutionized various domains like optimization, cryptography, machine learning, and simulation. With the increasing research in this significant computational field, it is becoming more essential to critically assess that how quantum algorithms are being employed in real-world problem-solving contexts. The comprehensive literature review that has been conducted in this study is primarily based upon the critical assessment of four key quantum algorithms, which are 1) Shor's Algorithm, 2) Grover's Algorithm, 3) the Quantum Approximate Optimization Algorithm (QAOA), and the 4) Quantum Fourier Transform (QFT). We have primarily focused upon their uses in secure communication, machine learning, chemistry, cryptography, and optimization. 30 excellent studies published between 2015 and 2024 were found through a systematic search of IEEE Xplore, SpringerLink, ScienceDirect, arXiv, and Google Scholar. Our results identify major patterns in the usage of algorithms like Grover's, Shor's, QAOA, and VQE and elucidate their applications in tackling theoretical and pragmatic problems. Unlike previous reviews that concentrate narrowly on algorithm design or on particular areas, this SLR presents a wide but organized synthesis that highlights problem-driven applications. Moreover, we have also highlighted the primary research gaps in this area and also suggested possible future directions for further investigation. This review provides a basis for researchers who would like to apply quantum algorithms to new or interdisciplinary problems.
The quantum annealing technique is such a paradigm that is next generation that can be used in solving optimization problem across different scientific domains like finance and material science and is capable of breaking through past obstacles to generate solutions for problems waiting to be solved. The purpose of this paper is the detailed depiction of the application of the quantum annealing in the optimization that includes the principles, applications, methodologies and outcomes and future trends all of them. Through a multidisciplinary approach, we examine the use of quantum annealing, as the method that exploits the specific features of the quantum annealing through the quantum mechanics, to explore the multitude of solution spaces faster and better than the classical methods, leading to the superior solution quality and the faster computing times. Next, we give examples for its implementation in Portfolio optimization, Machine learning, chemical optimization, Traffic management and so on depicting its role in delivering tangible benefits such as higher financial returns, precision improvement, and time savings contrast to the classical methods. Therefore, we also emphasize the current impediments, such as the scaling down the size and the ethical aspect, through which we give the way forward for the subsequent scientific researches and studies. In this abstract, we aim to depict the helix-like nature of quantum annealing in the revolution of optimization, bringing in innovations, and thus, shaping the future in which it is the quantum computing technologies that hold the central stage in addressing the complex interconnecting real-world issues.
This study examines the relationship between machine learning and quantum computing, emphasizing the potential benefits of quantum algorithms for classification, optimization and clustering problems. Through a comprehensive literature review of peer-reviewed journal articles and preprints from 2014 to 2024, Quantum K-Means Clustering, Quantum Support Vector Machines (QSVMs), and Quantum Approximate Optimization Algorithms (QAOA) and Quantum Annealing are among the important quantum algorithms identified in the study. Although the theoretical potential of these algorithms is substantial, present hardware constraints, such as noise, de-coherence, and qubit count limitations, make practical implementation difficult. The review also highlights the ongoing challenges in quantum error correction and the nascent stage of quantum hardware development, which prevent large-scale machine learning tasks from being fully realized. Even so, hybrid quantum-classical models are a plausible route forward for near-term utility. These results suggest that to leverage quantum machine learning to its full potential, further progress in quantum hardware, error correction codes, and hybrid algorithms is required. Future studies should focus on designing more robust quantum error correction methods, further developing hybrid systems and exploring new areas of machine learning, such as reinforcement learning and generative models.