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Open Quantum Systems Decoherence Quantum Machine Learning Quantum Simulation

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

arXiv
Authors: J. J. Torres, E. Romera

Year

2026

Paper ID

5800

Status

Preprint

Abstract Read

~2 min

Abstract Words

77

Citations

N/A

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.

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  • We present a biologically inspired quantum neural network that encodes neuronal populations as fully connected qubits governed by the Lipkin-Meshkov-Glick (LMG) quantum...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2602.16003 [2] arXiv https://arxiv.org/abs/2602.16003 [3] Publisher https://arxiv.org/abs/2602.16003

Local Citation Graph (Related-Paper Links)

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External citation index: OpenAlex citation signal

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