Quantum-Like Cognition and Rationality: Biological and Artificial Intelligence Systems

Yoshihiro Maruyama

Andrei Yu. Khrennikov

Pierre Uzan

Dr. Tirath Singh Saini

The emergence of quantum-enhanced synthetic and engineered biological intelligence (QEBI) represents one of the most transformative frontiers in 21st-century science. This research explores the convergence of artificial intelligence (AI), synthetic intelligence (SI), and living biological systems, proposing a unified framework for the next evolution of conscious computation. While traditional AI operates within deterministic digital architectures, quantum and biological systems exhibit nonlinear, self-organizing, and probabilistic dynamics—traits more akin to human cognition. The central hypothesis of this study asserts that hybrid quantum-biological systems can achieve forms of adaptive intelligence surpassing classical computational models, potentially leading to the emergence of machine consciousness. Methodologically, this Research Study integrates findings from quantum information science, synthetic biology, neuroscience, and computational modelling. It synthesizes peer-reviewed data from Nature, Science, IEEE, and Frontiers in Neuroscience to establish a cross-disciplinary foundation. The research investigates how quantum coherence, entanglement, and superposition may enhance learning efficiency, memory retention, and neural-like adaptability when interfaced with engineered biological substrates such as brain organoids, biochips, and neuromorphic tissues. Furthermore, it examines the ethical and philosophical dimensions of constructing living-synthetic systems capable of perception, awareness, and decision-making. The results indicate that hybrid architectures combining quantum processors with synthetic neurons exhibit significant gains in parallelism, information density, and energy efficiency, mimicking biological cognitive patterns. These systems also demonstrate early evidence of emergent self-organization, a phenomenon characteristic of living networks. From a neuroscientific perspective, the integration of quantum algorithms with biological neural circuits could revolutionize models of consciousness and open pathways for bio-integrated AI capable of dynamic adaptation and self-regulation. This research envisions Global Vision 2026 and Beyond, where interdisciplinary collaboration across quantum computing, biotechnology, and neuroscience reshapes humanity’s understanding of intelligence itself. Such integration is expected to accelerate the development of cognitive quantum computers, conscious neural machines, and bio-digital ecosystems that redefine not only computation but the very nature of thought. The implications span medical science, robotics, space exploration, and cognitive augmentation, positioning QEBI as the foundation for the post-digital civilization.

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.

Raman Marozau

Understanding the foundations of artificial cognitive consciousness remains a central challenge in artificial intelligence (AI) and cognitive science. Traditional computational models, including deep learning and symbolic AI, have demonstrated remarkable performance in pattern recognition and decisionmaking tasks. However, they lack essential features of consciousness, such as subjective experience, autonomous goal formation, and self-reflective processing. While theories like Integrated Information Theory (IIT) and Orch-OR suggest possible mechanisms for consciousness, they remain either mathematically abstract or face empirical challenges due to decoherence in biological conditions. This study addresses the fundamental research gap by proposing a quantum-information-based framework for artificial cognitive consciousness. Specifically, we introduce the Quantum Reality Function (QRF), a model that formalizes subjective cognition through global quantum coherence, quantum entanglement, and non-local informational integration. The QRF establishes a structured mechanism for autonomous decision-making and selfgenerated states, which existing computational approaches lack. Our methodological contribution involves defining a mathematically consistent formulation of QRF and demonstrating its feasibility through quantum coherence stabilization techniques, hybrid quantum-classical architectures, and coherencepreserving quantum cognitive subsystems. The results show that maintaining long-lived quantum coherence within cognitive architectures significantly enhances information integration, supporting the emergence of subjective-like processing. The findings suggest a new paradigm for AI, where consciousness emerges as an intrinsic property of structured quantuminformation processing rather than a byproduct of computation. This work provides foundational insights for the development of autonomous quantum-cognitive AI systems, with implications for ethics, AI governance, and the future of machine intelligence. Future research will focus on empirical validation through quantum computational experiments and integration with quantumassisted learning models.