QuanConNet: A Proposed Quantum-Inspired Deep Learning Framework for Modelling Non-Local Consciousness Field Synchronisation — A Systematic Review and Theoretical Framework

A systematic review of 54 studies across quantum consciousness, inter-brain synchrony, quantum machine learning, and non-local biofield interactions identified a computational gap: no existing framework can detect or model non-local, quantum-mediated correlations between human consciousness fields. The authors propose QuanConNet, a hybrid quantum-classical deep learning architecture with Quantum Entanglement-Inspired Attention layers, Variational Quantum Correlation Circuits, and a Consciousness Field Synchronisation Loss function. Theoretical analysis suggests computational advantages for high-dimensional EEG signals. An experimental protocol with 60 dyads is provided for validation. The work establishes a theoretical and architectural foundation for a new sub-field at the intersection of quantum AI and computational neuroscience.

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Abstract

Background: The question of whether human consciousness exhibits non-local, quantum-mediated inter-personal correlations represents one of the most profound and computationally unexplored frontiers at the convergence of quantum physics, neuroscience, and artificial intelligence. While quantum entanglement has been experimentally validated at the subatomic level, and while the Orchestrated Objective Reduction (Orch-OR) theory proposes quantum processes as the substrate of conscious experience, no computational framework has been proposed that can formally detect, model, or quantify non-local inter-individual consciousness field correlations using modern deep learning. Methods: We conducted a systematic review of 54 peer-reviewed studies published between 1982 and 2025 across four domains: quantum consciousness and Orch-OR theory, inter-brain synchronisation and hyperscanning, quantum machine learning architectures, and non-local biofield interactions. Following PRISMA 2020 guidelines, we identified a critical computational gap and propose QuanConNet — a novel hybrid quantum-classical deep learning architecture comprising Quantum Entanglement-Inspired Attention (QEIA) layers, Variational Quantum Correlation Circuits (VQCC), and a Consciousness Field Synchronisation Loss (CFSL) function grounded in quantum information theory. Results: Our systematic review identified 54 relevant studies, of which 31 reported statistically significant inter-individual neurophysiological correlations beyond classical explanations. Theoretical complexity analysis demonstrates that QuanConNet's non-separable attention formulation provides O(n log n) computational advantages over classical cross-subject attention for high-dimensional EEG signals. A proposed experimental protocol with power analysis (n=60 dyads, power=0.85, alpha=0.05) is provided for empirical validation. Conclusion: QuanConNet represents the first formally specified quantum-inspired computational framework for consciousness field modelling. This work establishes the theoretical foundation, architectural specification, and experimental roadmap for a new sub-field at the intersection of quantum AI and computational neuroscience. The full architecture specification and proposed experimental protocol are made publicly available to facilitate community replication.