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The Advantage and Disadvantage of Development of Quantum Computing in Machine Learning
- 1 College of Science, Mathematics and Technology, Wenzhou-Kean University, Wenzhou, Zhejiang Province, China, 325000
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Abstract
Quantum computing is an emergent technology with great promise to reshape machine learning. It enables computations intractable by any classical system. This paper discusses the basic concepts of quantum computing, its advantages, and their limitations regarding ML. It can be concluded that quantum computers leverage phenomena in quantum mechanics, such as superposition, entanglement, and interference execute those computations that are beyond the capability of classical systems. A few of the most important fancies in QSVM, QPCA, and QNN perform such tasks as computing kernel matrices, high-dimensional data processing, and solving optimization problems in some key points. In contrast, several formidable challenges persist, beginning with the scalability and coherence of qubits as hardware-related issues, and extending to software-centric problems such as data encoding and the efficiency of specific algorithmic applications. This paper also underscores potential future avenues for advancement, including the development of hybrid quantum-classical algorithms, quantum error correction techniques, and quantum-native machine learning models, aimed at surmounting these limitations. By addressing these challenges and directing focus towards prospective research, quantum computing holds the promise of unlocking transformative breakthroughs in various domains such as big data processing, drug development, financial modeling, and natural language processing. Such advancements could firmly establish quantum computing as the cornerstone of next-generation artificial intelligence technologies.
Keywords
quantum computing, machine learning, quantum machine learning, quantum algorithms
[1]. Long Haiquan, Liu Zhuoyue, and Wang Xiangtuan (2024). Research on the impact of the rapid development of artificial intelligence on computing power and energy China Informatization (11), 7-8
[2]. The physical implementation of quantum computation. (2000b). ADS. https://doi.org/10.1002/1521-3978(200009)48:9/11
[3]. Wang, Q. (2022, June). Support vector machine algorithm in machine learning. In 2022 IEEE international conference on artificial intelligence and computer applications (ICAICA) (pp. 750-756). IEEE.
[4]. Zhou, X., Yu, J., Tan, J., & Jiang, T. (2024). Quantum kernel estimation-based quantum support vector regression. Quantum Information Processing, 23(1). https://doi.org/10.1007/s11128-023-04231-7
[5]. Mandelbaum, R., Corcoles, A. D., & Gambetta, J. (2024, September 11). IBM’s Big Bet on the Quantum-Centric Supercomputer. IEEE Spectrum. https://spectrum.ieee.org/ibm-quantum-computer-2668978269
[6]. Zaman, A., Morrell, H. J., & Wong, H. Y. (2023). A Step-by-Step HHL algorithm walkthrough to enhance understanding of critical quantum computing concepts. IEEE Access, 11, 77117–77131. https://doi.org/10.1109/access.2023.3297658
Cite this article
Zhao,R. (2025). The Advantage and Disadvantage of Development of Quantum Computing in Machine Learning. Applied and Computational Engineering,119,167-172.
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Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning
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