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Quantum-Inspired Algorithms for AI and Machine Learning
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Author(s): Kamaleswari Pandurangan (Department of CSE, Er. Perumal Manimekalai College of Engineering, India), A. Priyadharshini (Jain School of Engineering and Technology, Jain University (Deemed), India), Rakheeba Taseen (School of CSE, Presidency University, India), B. Galebathullah (Department of CSE, Madanapalle Institute of Technology and Science, India), Haseeba Yaseen (Department of IT, Vasavi College of Engineering, India)and P. Ravichandran (PMC College of Engineering, India)
Copyright: 2025
Pages: 14
Source title:
Integration of AI, Quantum Computing, and Semiconductor Technology
Source Author(s)/Editor(s): Brojo Kishore Mishra (NIST University, Berhampur, India)
DOI: 10.4018/979-8-3693-7076-6.ch004
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Abstract
Quantum-inspired algorithms help handle complex optimization and inference challenges in AI and ML. Quantum-like algorithms based on quantum mechanics address challenges classical computer paradigms can't. These algorithms innovate data handling, solution finding, and pattern recognition. Quantum effects like superposition, entanglement, and tunneling influenced them. In particular, they work in parallel to explore large solution spaces and speed up processing for large datasets. Next, quantum-inspired algorithms including quantum annealing, optimization, and neural networks are discussed. Simulated annealing and the quantum approximate optimization algorithm (QAOA) make combinatorial optimization tasks in AI and ML easier. Quantum-inspired evolutionary and swarm intelligence algorithms tackle multi-modal optimization issues quickly. Furthermore, quantum-inspired neural networks (QNNs) have revolutionized deep learning. QNNs use quantum gates and quantum circuits to improve classification, regression, and generative modeling.
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