Quantum Inspired Computational Intelligence: Research and Applications

A. S. Manju, M. J. Nigam

Mukesh Kumar, S. Kalaiselvi, Manabhanjan Sahu +1 more

The purpose of this research is to the operation of artificial intelligence (AI) methods that are inspired by the concept of Quantum-inspired optimization for healthcare networks. The concepts of quantity mechanics served as the inspiration for these optimization approaches, which give promising outcomes for addressing complicated optimization challenges that are encountered in healthcare systems. The goal of Quantum-inspired optimization algorithms is to efficiently search for optimal outcomes for healthcare-related tasks such as patient scheduling, resource allocation, and treatment planning. These algorithms make use of the important community and probabilistic nature of Quantum computing. Healthcare networks can improve decision-making processes, improve resource application, and ultimately improve patient issues if they make use of the power of Quantum-inspired optimization. The purpose of this research is to investigate the inherent benefits and obstacles of introducing Quantum-inspired optimization into artificial intelligence for healthcare networks.

Rajesh Palthya

The increasing complexity of artificial intelligence (AI) has created a need for effective optimisation of highdimensional, nonlinear, and computationally expensive problems. Quantum-inspired optimisation provides an alternative approach by employing quantum-inspired representations and search mechanisms within classical computing systems. This review examines major approaches, including Quantum-Inspired Evolutionary Algorithms (QIEA), Quantum-Inspired Genetic Algorithms (QIGA), Quantum-Inspired Particle Swarm Optimisation (QIPSO), quantum annealing-related approaches, and hybrid methods. Their applications in machine learning, feature selection, hyperparameter optimisation, reinforcement learning, healthcare, finance, cybersecurity, robotics, and smart manufacturing are reviewed. Particular emphasis is placed on solution quality, convergence behaviour, computational cost, scalability, robustness, and reproducibility. The review identifies inconsistent benchmarking, parameter sensitivity, computational overhead, and limited reproducibility as major challenges. The findings indicate that quantum-inspired optimisation should not be considered universally superior to classical optimisation, as its effectiveness depends on problem structure and evaluation conditions. Future research should focus on scalable, hybrid, explainable, and reproducible optimisation frameworks for realistic large-scale AI applications.

Naoki Masuyama, Chu Kiong Loo

Quantum-Inspired Computational Intelligence based on quantum postulates is an emerging research area that exploit the parallelism of quantum mechanics. However, existing research efforts are limited to theoretical simulations and has not been implemented in robot application. With regards as Human-Robot Interaction, associative memory become essential for mutual communication. However, associative memory always suffers from limited memory capacity and high sensitivity to noise and the ability to recall from multi-modal sensory inputs. In this paper, we propose a Quantum-Inspired Multidirectional Associative Memory. This is the first attempt to overcome these two problems effectively with robot application.

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