This article comprehensively reviews the recently developed quantum mechanics-based optimization techniques. The quantum theory has been employed to speed up the evolutionary method and enhance the possibility of finding the optimal solution of conventional optimization techniques. Current developments in quantum computing have demonstrated that quantum theory can offer significant benefits over conventional theory for certain optimization techniques. The future prospective and benefits and drawbacks of this fresh category of quantum optimization techniques have been presented in this review. It also enables new researchers and algorithm developers to use these simple but extremely effective algorithms for problem-solving. This paper aims to give readers an overview of the fundamental elements and recent advances in optimization techniques so that they can develop and implement these for various applications. Finally, a few findings are reached, and future study on quantum-based optimization techniques is discussed.This article comprehensively reviews the recently developed quantum mechanics-based optimization techniques. The quantum theory has been employed to speed up the evolutionary method and enhance the possibility of finding the optimal solution of conventional optimization techniques. Current developments in quantum computing have demonstrated that quantum theory can offer significant benefits over conventional theory for certain optimization techniques. The future prospective and benefits and drawbacks of this fresh category of quantum optimization techniques have been presented in this review. It also enables new researchers and algorithm developers to use these simple but extremely effective algorithms for problem-solving. This paper aims to give readers an overview of the fundamental elements and recent advances in optimization techniques so that they can develop and implement these for various applications. Finally, a few findings are reached, and future study on quantum-based optimization techniques is discussed.

International Conference in Advances in Power, Signal, and Information Technology (APSIT)Jun 9, 20231 citation

Exploring the Versatility of QAOA: A Comprehensive Review

Rajavardhan Kashapogu, Saima Hasib, Akhtar Rasool

Quantum Approximate Optimization Algorithm (QAOA) is the notable advancement in quantum computing, which allows us to solve optimization problems differently. In this paper, we provide a detailed investigation of the possible applications of QAOA accompanied by an overview of previous implementations. This is where QAOA shines and can spin out solutions where ordinary methods takes millennia to converge with acceptable results for NP-hard optimization problems that involve transfer of $\mathbf{n}$ qubits between a quantum state. We present a comprehensive literature review in this paper, and potential areas of application where QAOA could be successfully used such as logistics, finance and Artificial Intelligence.Quantum Approximate Optimization Algorithm (QAOA) is the notable advancement in quantum computing, which allows us to solve optimization problems differently. In this paper, we provide a detailed investigation of the possible applications of QAOA accompanied by an overview of previous implementations. This is where QAOA shines and can spin out solutions where ordinary methods takes millennia to converge with acceptable results for NP-hard optimization problems that involve transfer of $\mathbf{n}$ qubits between a quantum state. We present a comprehensive literature review in this paper, and potential areas of application where QAOA could be successfully used such as logistics, finance and Artificial Intelligence.

International Conference on Computing, Communication and Networking Technologies (ICCCNT)Jun 24, 20246 citations

Quantum optimization techniques and applications

Euimin Lee, Shiho Kim

Advances in computersJul 23, 20250 citations

Quantum Algorithms for Optimization Problems

Dilnoz Muhamediyeva, Rustam Raximov, Shavkat Urokov

Lecture notes in computer scienceJul 29, 20250 citations

Quantum Computing and Its Applications in Artificial Intelligence: A Comprehensive Review

Atharva Bhatye

Quantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural NetworksQuantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural Networks

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTJun 25, 20251 citation

\mathbf{n}

qubits between a quantum state. We present a comprehensive literature review in this paper, and potential areas of application where QAOA could be successfully used such as logistics, finance and Artificial Intelligence.