Applications of quantum inspired computational intelligence: a survey

Siddhartha Bhattacharyya, Ujjwal Maulik, Paramartha Dutta

Priyanka Manchanda

In this paper, the survey of the use of quantum computers in the field of Artificial intelligence has been done. The laws of quantum computation and their application in the field of neural networks, machine learning and speech processing are discussed. Further, the concepts of Quantum Neural Networks, Entangled Neural Networks, Quantum Machine Learning Algorithms and Quantum Support Vector Machine are explained.

Yangyang Li, Mengzhuo Tian, Guangyuan Liu +2 more

Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios and brand-new vitality. Inspired by nature, intelligent algorithm has always been one of the research hotspots. It is a frontier interdisciplinary subject with a perfect integration of biology, mathematics and other disciplines. Naturally, the idea of combining quantum mechanism with intelligent algorithms will inject new vitality into artificial intelligence system. This paper lists major breakthroughs in the development of quantum domain firstly, then summarizes the existing quantum algorithms from two aspects: quantum optimization and quantum learning. After that, related concepts, main contents and research progresses of quantum optimization and quantum learning are introduced respectively. At last, experiments are conducted to prove that quantum intelligent algorithms have strong competitiveness compared with traditional intelligent algorithms and possess great potential by simulating quantum computing.

Dr. Rakshitha Kiran P Dr. Samitha Khaiyum Dr. Pallavi Kulkarni Dr. Pavithra G. Shetty Prof. Raksha Kodnad R

Sakthivel N, Balaji M, Thogai Rani B +3 more

The rapid growth of ultra-large and highdimensional datasets has intensified the demand for deep learning (DL) models that are scalable, accurate, transparent, and interpretable. In parallel, quantum-inspired computing has emerged as a promising paradigm that emulates quantum principles such as superposition, entanglement, and probabilistic state evolution without requiring quantum hardware. This study presents a comprehensive survey of explainable quantum-inspired DL models for large-scale data mining. Existing quantum-inspired architectures integrated with deep neural networks (DNN), including convolutional, recurrent, and attention-based models, are systematically analyzed alongside explainable artificial intelligence (XAI) techniques to enhance interpretability and trustworthiness. The reviewed methods are classified based on learning paradigms, explainability mechanisms, scalability strategies, and application domains such as healthcare analytics, cybersecurity, intelligent transportation, and industrial big data systems. Furthermore, evaluation metrics, computational complexity, and performance trade-offs are examined in comparison with conventional DL approaches. Finally, key open challenges and future research directions for real-world large-scale deployment are highlighted.