Ramouthar, Ravindra (2025) Quantum Superintelligence: Quantum Cognitive Frameworks with Applications in Complex Dynamic Environments. Doctoral thesis, University of Staffordshire.
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Abstract or description
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.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Faculty: | PhD |
| Depositing User: | Library STORE team |
| Date Deposited: | 19 Dec 2025 16:18 |
| Last Modified: | 22 Dec 2025 14:43 |
| URI: | https://eprints.staffs.ac.uk/id/eprint/9497 |
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