This research presents a Quantum-Inspired AI model designed to solve grid-based puzzles like Sudoku more efficiently. By combining classical AI techniques with quantum-inspired optimization methods such as QAOA and VQE, the model enhances problem-solving speed and accuracy. It integrates deep learning, reinforcement learning, and symbolic reasoning to mimic human cognitive processes. The system is capable of handling structured logic and adapting across various puzzle formats. Results show improved performance over traditional AI Models. This approach has a pplications in education, cybersecurity, and AI-assisted decision-making. The model also incorporates Quantum Neural Networks (QNNs) for better pattern recognition and learning in ambiguous or incomplete puzzle states. A modular framework built with tools like Qiskit and TensorFlow Quantum allows for easy experimentation and hybrid integration. Performance evaluations highlight gains in both computational efficiency and interpretability. The system demonstrates robustness across different puzzle complexities and constraint types. Its scalability and adaptability make it suitable for real-world problem-solving in logistics, planning, and intelligent tutoring systems.
This research presents a Quantum-Inspired AI model designed to solve grid-based puzzles like Sudoku more efficiently. By combining classical AI techniques with quantum-inspired optimization methods such as QAOA and VQE, the model enhances problem-solving speed and accuracy. It integrates deep learning, reinforcement learning, and symbolic reasoning to mimic human cognitive processes. The system is capable of handling structured logic and adapting across various puzzle formats. Results show improved performance over traditional AI Models. This approach has a pplications in education, cybersecurity, and AI-assisted decision-making. The model also incorporates Quantum Neural Networks (QNNs) for better pattern recognition and learning in ambiguous or incomplete puzzle states. A modular framework built with tools like Qiskit and TensorFlow Quantum allows for easy experimentation and hybrid integration. Performance evaluations highlight gains in both computational efficiency and interpretability. The system demonstrates robustness across different puzzle complexities and constraint types. Its scalability and adaptability make it suitable for real-world problem-solving in logistics, planning, and intelligent tutoring systems.
Quantum computing and artificial intelligence (AI) are positioned as groundbreaking tools capable of tackling complexities inherent to modern computation posed by high-dimensional optimization problems.This research presents a quantum-enhanced optimization framework that leverages the QAOA (Quantum Approximate Optimization Algorithm) and VQE (Variational Quantum Eigen solver) within a hybrid quantum-classical paradigm.Machine learning models, including reinforcement learning and NN (neural networks), are integrated to optimize the quantum circuit parameters, enhance convergence, and improve scalability for complex problem spaces.Experimental evaluations demonstrate that this approach outperforms traditional optimization techniques in terms of efficiency and solution accuracy, particularly for large-scale multi-objective tasks.The framework is applied to key domains such as smart energy systems, logistics, and scientific modeling, illustrating its versatility and alignment with sustainable development goals.This study also explores the limitations of current quantum hardware and discusses future pathways for advancing hybrid quantum AI systems, offering significant contributions to next generation computational methodologies and intelligent problem solving.
The rapid advancement of artificial intelligence (AI) and quantum computing has introduced new paradigms for solving computationally complex problems beyond the capabilities of classical systems. This paper presents a Quantum Intelligence Platform (QIP), a hybrid quantum-inspired computational framework designed to enhance intelligent decision prediction and optimization in complex environments. The proposed system integrates quantum probabilistic modelling concepts such as superposition and interference with classical machine learning algorithms to improve prediction accuracy and adaptive decision-making. A hybrid architecture combining quantum circuit simulation and classical deep learning modules is developed to enable scalable experimentation without requiring physical quantum hardware. The platform is evaluated on decision prediction and optimization tasks, demonstrating improvements in convergence stability and predictive performance compared to conventional models. Experimental results show that quantum-inspired state representation enhances feature expressiveness and probabilistic reasoning. The proposed framework bridges the gap between theoretical quantum computation and practical intelligent systems, offering a scalable pathway for future quantum-enhanced AI applications.
A promising combination of artificial intelligence and quantum computing, quantum neural networks (QNNs) have the potential to completely transform the way neural networks interpret and learn from data. QNNs can outperform classical neural networks in terms of speed, efficiency, and scalability by utilizing the special qualities of quantum physics, such as superposition, entanglement, and quantum parallelism. The basic ideas of QNNs, their structures, and the ways in which quantum algorithms can be incorporated into machine learning processes to improve problem-solving skills are all examined in this study. current developments in QNNs, emphasizing how they can be used for challenging applications including pattern recognition, optimization, and high-dimensional data space decision-making. We also go over the difficulties in creating scalable QNN models and the possible future applications of QNNs in artificial intelligence. By utilizing the potential of quantum computing, QNNs have the potential to advance artificial intelligence by providing answers to issues that are now intractable through traditional methods.
Abstract: This research explores the emerging paradigm of Quantum Intelligence (QI), which unifies artificial intelligence and quantum computing to enable next-generation cognitive systems. By leveraging quantum principles such as superposition, entanglement, and quantum tunneling, QI frameworks transcend classical AI limitations in learning efficiency, contextual awareness, and problem-solving complexity. The study implements a hybrid architecture integrating variational quantum circuits with reinforcement learning agents, tested across multi-goal environments. Experimental results reveal faster convergence, greater entropy reduction, and enhanced generalization compared to classical deep Q-networks. These findings demonstrate the transformative potential of QI to replicate and surpass human-like cognition, offering scalable, efficient, and adaptable models for future autonomous reasoning systems. Keywords: Quantum intelligence, Artificial intelligence, Quantum computing, Hybrid AI systems, Reinforcement learning, Variational quantum circuits, Cognitive systems, Quantum-enhanced learning, Entropy optimization, Next-generation AI
Artificial Intelligence (AI) and quantum computing are converging to form a new era of ultra-fast computational problem-solving. Quantum optimization algorithms—when integrated with AI-driven heuristics, reinforcement learning, and neural-guided search models—offer unprecedented capabilities for tackling complex problems such as NP-hard optimization, logistics scheduling, cryptographic analysis, and large-scale simulation tasks. This research investigates the combined potential of AI-augmented quantum algorithms, focusing on how machine learning enhances qubit efficiency, error mitigation, quantum annealing performance, and adaptive gate-level optimization. A practical case study evaluates hybrid AI-quantum models applied to combinatorial optimization tasks, demonstrating significant reductions in computational time and improved solution accuracy. Findings highlight a future in which AI-enhanced quantum systems redefine computation, enabling breakthroughs in science, industry, and national innovation systems
The rapid advancement of Artificial Intelligence (AI) has heightened concerns about the opacity of decision-making processes in complex models, particularly deep neural networks. This research explores a novel paradigm that integrates quantum computing principles into Explainable AI (XAI) to enhance interpretability without compromising predictive accuracy. The study introduces a Quantum-inspired Interpretable Model (QIIM) framework that leverages Hilbert space embeddings, unitary transformations, and operator-based learning to represent knowledge in a mathematically transparent form. The proposed model computes interpretability through the expectation values of observable operators, enabling the decomposition of decisions into quantifiable, human-understandable components. To capture hierarchical relationships, a Tensor Network Interpretable Model (TNIM) is further developed, offering scalable insights into complex dependencies among features. Experimental evaluations—performed on benchmark datasets for decision-making tasks—demonstrate that the quantum-inspired models achieve competitive accuracy while significantly improving local and global interpretability metrics compared to classical XAI techniques. The findings underscore the potential of quantum formalism as a new foundation for transparent AI systems, bridging the gap between computational efficiency and explainability. This study contributes to both theoretical understanding and practical advancement in interpretable machine learning, paving the way for ethically aligned, transparent, and human-trustworthy AI-driven decision support systems.