QuanConNet: A Proposed Quantum-Inspired Deep Learning Framework for Modelling Non-Local Consciousness Field Synchronisation — A Systematic Review and Theoretical Framework
Zenodo (CERN European Organization for Nuclear Research) May 23, 2026 Shoeb Ahmad
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.