Quantum computing promises to revolutionize computation by leveraging superposition, entanglement, and quantum interference. This paper examines whether it can bridge the ontological divide between processual reality (Monos) and representational structures (Logos) in the context of Artificial General Intelligence (AGI). Drawing on Metamonism CORE = v1.3, we argue that quantum systems, as currently conceived, accelerate stabilization rather than prevent it, remaining trapped in Logos-dominant paradigms.We distinguish the illusion of an ontological bridge—where quantum processuality serves classical fixation—from a genuine integration that could enable enforced Unfold. While quantum dynamics approximates Monos, measurement enforces Logos, making quantum computing a more efficient optimizer rather than a pathway to non equilibrium cognition.This analysis reframes quantum computing not as an AGI enabler but as a test case for ontological viability in intelligence research and establishes it as a prime example of accelerated fixation, disqualifying it from consideration as a foundation for non-equilibrium cognition.
Quantum computing promises to revolutionize computation by leveraging superposition, entanglement, and quantum interference. This paper examines whether it can bridge the ontological divide between processual reality (Monos) and representational structures (Logos) in the context of Artificial General Intelligence (AGI). Drawing on Metamonism CORE = v1.3, we argue that quantum systems, as currently conceived, accelerate stabilization rather than prevent it, remaining trapped in Logos-dominant paradigms.We distinguish the illusion of an ontological bridge—where quantum processuality serves classical fixation—from a genuine integration that could enable enforced Unfold. While quantum dynamics approximates Monos, measurement enforces Logos, making quantum computing a more efficient optimizer rather than a pathway to non equilibrium cognition.This analysis reframes quantum computing not as an AGI enabler but as a test case for ontological viability in intelligence research and establishes it as a prime example of accelerated fixation, disqualifying it from consideration as a foundation for non-equilibrium cognition.
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.
The future of Artificial General Intelligence (AGI) lies in the fusion of quantum computing and artificial intelligence, creating a synergistic framework capable of human-like cognitive reasoning and adaptability. This study examined how quantum–AI integration enhances computational efficiency, learning capacity, and contextual understanding beyond the limits of classical AI systems. The findings highlighted that quantum-inspired neural networks, through their ability to process complex data in parallel, can enable AGI systems to simulate decision-making, perception, and self-learning in a manner closer to human cognition. Moreover, the research emphasized the ethical and governance challenges that accompany such advancements, including issues of transparency, control, and the moral implications of autonomous systems. The study proposed that responsible innovation, combined with interdisciplinary collaboration, is essential for aligning AGI’s development with societal values and global policy frameworks. Overall, the quantum–AI synergy was found to be a critical driver toward achieving general intelligence, offering unprecedented potential for innovation across scientific, industrial, and educational domains while raising profound ethical considerations for the future of human–machine coexistence.
Quantum computing represents a transformative shift from classical computational paradigms by utilizing quantum mechanical principles such as superposition, entanglement, and quantum interference.In the era of Artificial Intelligence (AI), the integration of quantum computing with information technology opens new possibilities for solving complex computational problems beyond the capabilities of traditional systems.This paper explores the conceptual foundations of quantum computing and its convergence with AI-driven information technologies.It examines how quantum algorithms enhance machine learning efficiency, optimize large-scale data processing, and strengthen cybersecurity through quantum cryptography.Furthermore, the study discusses current technological challenges including hardware instability, error correction, scalability issues, and the need for interdisciplinary expertise.Using analytical and secondary data sources, the paper evaluates emerging applications in healthcare, finance, communication networks, and scientific simulations.The research concludes that quantum-AI integration will redefine computational intelligence, enabling faster decision-making and innovative technological solutions.The study highlights future research directions and emphasizes the importance of national-level academic collaboration to accelerate quantum technology adoption.
Abstract The gap between natural and artificial intelligence is often discussed in terms of creativity, contextual adaptability, and non-algorithmic decision-making capacities where human cognition appears fundamentally different from current AI systems. This paper argues that developing quantum and quantum-like models of cognition, decision-making, and AI provides a promising pathway for narrowing, and perhaps essentially bridging, this gap. Empirical studies of human cognition and decision-making reveal systematic deviations from classical probability, logic, and information theory—manifesting as contextuality, order effects, interference (such as conjunction and disjunction effects), task incompatibility, and apparent randomness. These phenomena are well captured by quantum probability theory and related quantum-like frameworks, which provide a rigorous mathematical formalism—Hilbert spaces, superposition, entanglement, and decoherence—for modeling cognitive states and their evolution. Such models go beyond metaphor, showing that aspects of human reasoning can be more faithfully represented using quantum-like rather than classical probabilistic structures. Although genuine quantum (based on quantum physics) and quantum-like approaches share the same mathematical foundation, they differ experimentally. Both stimulate the development of novel AI architectures: quantum AI (QAI) and quantum-like AI (QLAI). While QAI depends on advances in quantum computing, QLAI can be realized on classical digital or analog hardware. The advancement of both offers a promising route to reducing—and potentially bridging—the divide between natural and artificial intelligence. This paper sets out a conceptual program to unify natural and artificial intelligence via quantum/quantum-like models of consciousness/cognition and AI.
This paper explores the convergence of quantum computing and artificial intelligence (AI), examining how their integration may redefine computational paradigms. Quantum computing, with its unique properties of superposition and entanglement, has the potential to exponentially accelerate AI processes, particularly in optimization, machine learning, and data analysis. We investigate quantum algorithms, such as the quantum Fourier transform and Grover’s algorithm, highlighting their application to AI models and machine learning tasks that require vast computational resources. The paper further delves into hybrid quantum-classical approaches, which leverage the strengths of both domains to address real-world problems. Challenges, such as quantum error correction, scalability, and the need for specialized hardware, are also discussed. We provide an analysis of ongoing advancements in quantum AI, including quantum-enhanced neural networks and reinforcement learning, and their implications for fields like natural language processing and predictive analytics. This research emphasizes the transformative potential of quantum AI while acknowledging the significant technical hurdles that remain. The integration of quantum computing and AI promises to unlock unprecedented computational capabilities, paving the way for breakthroughs in scientific research, industry applications, and complex problem-solving.
Quantum computing and artificial intelligence (AI) are two of the most transformative technologies of the 21st century. The convergence of these fields promises unprecedented advancements in computational capabilities, enabling solutions to complex problems that are currently intractable for classical computers. This paper explores the intersection of quantum computing and AI, examining the potential impacts, current research, challenges, and future directions. By leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism, quantum computing can enhance AI algorithms, offering exponential speedups and new methodologies for machine learning, optimization, and data analysis.