Dynamic Synaptic Modulation of LMG Qubits populations in a Bio-Inspired Quantum Brain

TL;DR

This paper introduces a bio-inspired quantum neural network model using LMG qubits, demonstrating stable population control and oscillations, paving the way for quantum brain architectures.

Contribution

It proposes a novel quantum neural network framework with activity-dependent homeostatic control based on the LMG Hamiltonian, linking quantum many-body dynamics to neural functions.

Findings

Demonstrates stable quantum population set points

Shows controllable oscillations in quantum neural populations

Highlights robustness dependent on system size

Abstract

We present a biologically inspired quantum neural network that encodes neuronal populations as fully connected qubits governed by the Lipkin-Meshkov-Glick (LMG) quantum Hamiltonian and stabilized by a synaptic-efficacy feedback implementing activity-dependent homeostatic control. The framework links collective quantum many-body modes and attractor structure to population homeostasis and rhythmogenesis, outlining scalable computational primitives -- stable set points, controllable oscillations, and size-dependent robustness -- that position LMG-based architectures as promising blueprints for bio-inspired quantum brains on future quantum hardware.