Quantum Computational Models of Brain-Wide Functional Connectivity

Abstract

Modeling brain-wide functional connectivity remains one of the central challenges in computational neuroscience due to the enormous dimensionality, nonlinearity, and dynamical complexity of neural interactions. Classical network models, while effective for localized circuits, struggle to capture long-range dependencies, cross-frequency coupling, and emergent global patterns across billions of neurons and trillions of synapses. Quantum computational approaches offer a promising new direction by exploiting superposition, entanglement, and high-dimensional quantum state representations to encode whole-brain connectivity more efficiently. This study proposes a quantum-enhanced framework for modeling large-scale functional connectivity using hybrid quantum–classical graph algorithms, variational quantum neural models, and quantum kernel-based similarity mapping. By encoding temporal neural signals into quantum states and performing state-space exploration with variational circuits, the framework aims to reveal latent connectivity patterns, global synchronization structures, and cross-network dynamics that classical methods often fail to identify. Early simulation results suggest improved sensitivity to weak long-range interactions and enhanced ability to detect dynamic functional modules. The proposed approach highlights the transformative potential of quantum computation in advancing systems-level neuroscience, enabling more accurate whole-brain models and opening new opportunities for understanding cognition and neurological disorders.