Quantum Intelligence: A Foundational Framework for Post-Classical Cognition

Ravindra Ramouthar

This thesis operationalizes a quantum cognitive architecture for quantum superintelligence that is epistemologically distinct from classical paradigms. It introduces qPrime, a recursively self‑improving, autonomous, multi‑QIU quantum cognitive framework, and its embodied prototype, qZero, engineered for high‑stakes decision‑making in adversarial, dynamic environments. By fusing quantum mechanics, cognitive science, and artificial intelligence, this work advances a quantum superintelligence defined in a quantum cognition paradigm that models and enhances cognitive faculties via contextuality, superposition, entanglement, and non‑classical logic. Methodologically, the project synthesizes theoretical modelling, quantum algorithm design, and empirical validation. The experimental platform, Area 51 – Quantum Machinery and Superintelligence, integrates Quantum Deep Reinforcement Learning (qDRL) with IBMQ’s 127‑qubit Eagle processor. qZero is trained and evaluated in a simulated Knowledge‑Based Dynamic Environment (MOCK‑BDE) using real‑time signals from Call of Duty – Modern Warfare 2. Metrics include RMSE, MAPE, and a self‑evaluation model correlating enemy dispatch payloads with combat outcomes. Results show that qZero achieved a 186% improvement in decision‑efficacy over classical DRL baselines, with MAPE = 1.05% and recommendation accuracy > 97%. These findings substantiate that quantum‑enhanced cognition outperforms both human and classical AI systems in high‑dimensional, uncertain domains. The novelty lies in formalizing quantum superintelligence as a cognitive architecture composed of Quantum Intelligence Units (QIUs)—entities that are not merely computationally superior, but post‑classical in representational semantics and decision dynamics. This work introduces the pioneering qPrime Quantum Control Processor plane, a fixed‑length quantum controller circuit optimized for decision‑selection, and demonstrate the feasibility of multi‑QIU cognition as a unifying substrate for AGI/ASI trajectories. The implications are profound: intelligence modelled through quantum‑theoretic constructs transcends the symbolic/subsymbolic dichotomy, supports non‑decomposable reasoning, and establishes a foundation for collective, multi‑QIU superintelligence. This is, to my knowledge, among the first scaled implementations of quantum cognition, charting a post‑classical, post‑symbolic direction for AI into a new frontier of quantum superintelligence.

Mukund Sai Vikram Tyagadurgam, Vetrivelan Tamilmani, Sriram Pabbineedi +1 more

Diederik Aerts, Sandro Sozzo, Tomás Veloz

Elangovan Sivalingam

The exponential growth of data-driven systems has outpaced the computational capacity of conventional cloud infrastructures, demanding new paradigms of intelligence and optimization. This research introduces a quantum-enabled cloud intelligence framework that integrates post-classical optimization methods with data-centric artificial intelligence pipelines. The study investigates how quantum-inspired and hybrid quantum–classical models can enhance large-scale optimization, feature extraction, and decision processes within distributed cloud environments. By coupling quantum approximate optimization algorithms (QAOA) and variational quantum circuits (VQC) with classical data analytics layers, the framework achieves reduced computational latency, improved convergence for high-dimensional problems, and enhanced interpretability of AI-driven outcomes. A prototype quantum-cloud orchestration layer is developed to dynamically allocate quantum resources through cloud APIs, enabling adaptive workload balancing across heterogeneous architectures. Empirical results from simulation and hybrid execution demonstrate significant performance gains over classical baselines in terms of both efficiency and solution quality. Beyond performance metrics, the study emphasizes ethical and sustainable computing principles, ensuring that quantum acceleration aligns with transparent, explainable, and accountable AI operations. The outcomes position quantum-enabled cloud intelligence as a foundational technology for the next generation of data-centric optimization and cognitive automation.

Andrei Yu. Khrennikov

Xiangsheng Yu

Mainstream quantum mechanical interpretations, classical logical paradox resolution frameworks, cognitive theories of human deceptive behavior, and probabilistic artificial intelligence models universally exhibit a fundamental methodological flaw. These conventional approaches prioritize subjective observational phenomena and cognitively constructed appearances as theoretical presuppositions, thereby conflating the intrinsic determinacy of objective ontology with illusory uncertainty arising from incomplete information and limited cognitive capacity. This study systematically examines such paradigm-level biases by conducting a cross-disciplinary methodological comparison between prevalent theoretical frameworks and the dual paradigm integrating the binary excluded middle law and ontology–cognitive entropy. The proposed paradigm establishes the binary determinacy of objective ontology—independent of observation, reasoning, and subjective perception—as the foundational axiom. All apparent uncertainties, including quantum superposition, instantaneous quantum transitions, formal logical contradictions, cognitive ambiguity, and probabilistic model outputs, are attributed to transient cognitive entropy states induced by insufficient information convergence. The dynamic cognitive entropy metric complements the missing continuous evolutionary mechanism in traditional quantum theories and fundamentally resolves the inherent defects of appearance-oriented methodological frameworks. Cross-domain comparisons demonstrate that the ontology–cognitive entropy dual paradigm features ontology–cognition dichotomy, dynamic entropy quantification, and full logical self-consistency without subjective presuppositions. This framework provides a rigorous, unified, and objective theoretical foundation for addressing pervasive uncertainty problems in fundamental physics, formal logic, cognitive science, and artificial intelligence, supporting both basic theoretical innovation and engineering risk-control applications.