Modeling a Quantum AI with Free Will Based on Artificial Self-Awareness

Scott A. Humr, M. Canan, Mustafa Demir

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.

Essam Said Hanandeh, Shengxiang Zang, Jiajin Kang +6 more

Quantum artificial intelligence is an important step forward in the relationship with both quantum computing and AI. This paper investigates the use of quantum computing technique as an augmentation to AI capabilities efficiencies in data retrieval, model development, and problem resolution. A very general description is provided of self-consistence concepts of quantum computing, the main milestones in the field of quantum artificial intelligence, and practical analogs in a range of areas. Algorithm development, the development of hardware, and ethics are the obstacles that are mentioned, which then give some perspectives concerning the future within this new field.

Kathryn Blackmond Laskey

Satchidananda Dehuri, Monalisa Jena, Sarat Chandra Nayak +2 more

Hassaan Bin Umar, Ali Raza, Rabia Zafar +1 more

The integration of quantum computing and artificial intelligence (AI) has opened new frontiers for enhancing cognitive adaptability and advancing artificial general intelligence (AGI). This study explores the conceptual synergy between quantum–AI integration as an independent variable, cognitive adaptability as a mediating variable, and AGI cognitive performance as the dependent variable. By examining how quantum principles such as superposition and entanglement enhance AI’s capacity for non-linear reasoning, this research outlines a transformative model that aligns with human-like cognition. The study employs simulation-based analyses comparing classical and quantum–AI models across diverse cognitive tasks, revealing that quantum–AI systems outperform traditional AI in adaptability, problem generalization, and decision precision. The results indicate that quantum-enhanced learning enables faster adaptation and improved generalization, suggesting significant implications for cognitive modeling, human–machine collaboration, and AGI development. The theoretical contribution lies in expanding AGI’s cognitive architecture through quantum principles, while the practical implications include the development of adaptive, self-learning AI systems for complex, dynamic environments. Future research should explore empirical testing of quantum–AI frameworks, cross-disciplinary modeling between quantum cognition and neuroscience, and ethical considerations in autonomous decision-making systems.