Understanding human decision-making in uncertain and dynamic scenarios is essential for advancing technologies in AI, predictive analytics, and behavioral simulations. Fuzzy logic provides an approach for representing gradual transitions and capturing the nuances of human behaviors, making it a foundation for tackling such challenges. This study explores a hybrid approach integrating fuzzy logic, quantum computing, and machine learning to model complex decision-making scenarios. Using a case study of the Prisoner’s Dilemma, the methodology captures nuanced human behaviors and uncertainties through fuzzy membership degrees while leveraging quantum properties like superposition and entanglement to simulate dynamic agent interactions. The resulting simulations generated a comprehensive dataset, which was subsequently applied in a quantum-based artificial intelligence model, demonstrating the capability of this integrated approach to uncover complex patterns in uncertain systems. This work offers a foundation for advancing computational models in decision support systems and predictive analytics, highlighting the promise of emerging technologies in addressing challenges in multidimensional and interdependent environments.
Understanding human decision-making in uncertain and dynamic scenarios is essential for advancing technologies in AI, predictive analytics, and behavioral simulations. Fuzzy logic provides an approach for representing gradual transitions and capturing the nuances of human behaviors, making it a foundation for tackling such challenges. This study explores a hybrid approach integrating fuzzy logic, quantum computing, and machine learning to model complex decision-making scenarios. Using a case study of the Prisoner’s Dilemma, the methodology captures nuanced human behaviors and uncertainties through fuzzy membership degrees while leveraging quantum properties like superposition and entanglement to simulate dynamic agent interactions. The resulting simulations generated a comprehensive dataset, which was subsequently applied in a quantum-based artificial intelligence model, demonstrating the capability of this integrated approach to uncover complex patterns in uncertain systems. This work offers a foundation for advancing computational models in decision support systems and predictive analytics, highlighting the promise of emerging technologies in addressing challenges in multidimensional and interdependent environments.
This study introduces an innovative quantum fuzzy approach for modeling and simulating complex decision-making processes, utilizing quantum circuits to express membership degrees as unitary transformations. We contrast Quantum Fuzzy Computing with Classical Computing, highlighting advancements in modeling the prisoner's dilemma. Using the Qiskit framework, we demonstrate how quantum fuzzy algorithms featuring connectives such as ‘exclusive or’ and ‘arithmetic mean’ enable detailed modeling of multi-agent relationships via multidimensional quantum registers, showcasing potential advancements in this hybrid research area.
The increasing complexity of modern decision-making environments has challenged the effectiveness of classical mathematical models. Traditional probabilistic frameworks often struggle to represent uncertainty, ambiguity, contextual dependence, and nonlinear interactions among decision variables. Quantum-inspired mathematical models have emerged as a promising interdisciplinary approach that leverages mathematical principles derived from quantum theory without requiring quantum computing hardware. These models utilize concepts such as superposition, interference, entanglement, and contextuality to represent complex decision processes more accurately. This paper explores the theoretical foundations, mathematical structures, and applications of quantum-inspired decision models in complex systems. The study examines how quantum probability theory provides a more flexible representation of human cognition, organizational decision-making, financial forecasting, and artificial intelligence systems. Furthermore, the paper discusses mathematical formulations, implementation strategies, advantages, challenges, and future research directions. The findings suggest that quantum-inspired models offer significant potential for addressing uncertainty and dynamic interactions in complex decision environments, thereby contributing to the advancement of intelligent decision support systems.
Quantum Machine Learning (QML) has emerged as a groundbreaking approach within the rapidly evolving field of Quantum Computing (QC), leveraging quantum phenomena such as superposition and entanglement to surpass classical models in representational power, optimization speed, and computational efficiency. This paradigm shift paves the way for transformative applications across diverse domains while simultaneously confronting the inherent complexities of quantum mechanics and hardware limitations. Among the powerful computational models used for decision-making and pattern recognition, Fuzzy Cognitive Maps (FCMs) have demonstrated exceptional capabilities in modeling complex systems. Researchers have continually refined the traditional FCM framework by integrating novel features to enhance its effectiveness. However, despite its potential, the integration of quantum computing with FCMs remains largely unexplored, with only a limited number of studies addressing this intersection. This work provides a comprehensive theoretical algorithmic evaluation of the integration between Quantum Computing and FCMs, investigating potential advancements, challenges, and novel applications. Additionally, we present a systematic review of existing research on Quantum Fuzzy Cognitive Maps (QFCMs), offering new insights into their theoretical foundations and future directions. By bridging the gap between quantum computing and cognitive modeling, this study aims to unlock new possibilities for intelligent decision-making systems in the quantum era.
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.
Artificial Intelligence is set to encompass additional decision space that has traditionally been the purview of humans. However, this decision space remains contested. Incongruencies between artificial intelligence and human rationalization processes introduce uncertainties in human decision-making, which require new conceptualizations that capture these distinct types of uncertainties. Hence, developing new ways to model human and artificial intelligence interactions are necessary to account for such uncertainties and improve situation awareness and decision-making. In this paper, we outline current conceptualizations of human and machine rationalities. Next, we offer the concept of rational prediction deviations (via quantum probability theory) for capturing uncertainty in situational awareness. Lastly, we propose a human-in-the-loop construct to explicate how applications of quantum probability theory in decision science can ameliorate situation awareness models by providing a novel way to capture distinct dynamics of decision making.
Over the last 65 years, Artificial Intelligence has successfully transformed robots from simple machines into intelligent autonomous agents. However, as robotics enters high-stakes fields like "Quick Commerce" and complex engineering, classical computing relying on binary bits is hitting a massive "computational ceiling." These traditional systems are simply too slow to process the colossal, high-dimensional data required for instant decision-making. This paper reviews the evolution of AI and identifies the specific hardware bottlenecks limiting modern autonomy. To bridge this gap, we propose "Quantum Power" leveraging qubits, superposition, and entanglement as the essential engine for the next generation of robotics. By synthesizing the interplay between AI and Quantum Computing, this research demonstrates how a hybrid approach can overcome current logistical barriers, moving us toward a future of faster, smarter, and truly symbiotic quantum-powered robots. Despite its promising advantages, the implementation of quantum-AI integration faces several challenges, including limited availability of quantum hardware, noise sensitivity, and the need for hybrid quantum-classical frameworks. The study highlights ongoing research efforts and future directions aimed at overcoming these limitations. Overall, the fusion of quantum computing and AI offers a novel and transformative approach to developing intelligent robotic systems with superior learning and decision-making capabilities.