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Correction: Vallverdú, J.; Rius, G. NeuroQ: Quantum-Inspired Brain Emulation. <i>Biomimetics</i> 2025, <i>10</i>, 516.

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Updated: Sep 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

NeuroQ: Quantum-Inspired Brain Emulation.

Jordi Vallverdú¹, Gemma Rius²

¹Philosophy Department, ICREA-UAB, Bellaterra, 08193 Barcelona, Spain.

Biomimetics (Basel, Switzerland)

|August 27, 2025

Summary

This summary is machine-generated.

This paper introduces NeuroQ, a quantum-inspired framework for brain emulation using stochastic mechanics. It reformulates neuron models into a quantum-like formalism, enabling new simulation strategies for advanced neuromorphic computing.

Keywords:

FitzHugh–Nagumo model hamiltonian simulation neuronal planck constant quantum-inspired brain emulation stochastic mechanics

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Area of Science:

Computational Neuroscience
Quantum Physics
Theoretical Neuroscience

Background:

Classical computational models struggle to replicate the complex stochastic and nonlinear dynamics of biological neural systems.
Existing brain emulation methods lack the capacity to capture potentially coherent features inherent in neural processing.

Purpose of the Study:

Introduce NeuroQ, a novel quantum-inspired framework for brain emulation.
Establish a theoretical foundation for utilizing quantum simulation strategies in neuroscience.
Explore implications for neuromorphic hardware and artificial consciousness.

Main Methods:

Reformulation of the FitzHugh-Nagumo neuron model using stochastic mechanics (Nelson's formulation) with structured noise.
Derivation of a Schrödinger-like equation to represent neural membrane dynamics in a quantum-like formalism.
Conceptual application of quantum simulation techniques such as Hamiltonian encoding and variational eigensolvers.

Main Results:

A quantum-like formalism for neural dynamics was derived from a classical neuron model.
The NeuroQ framework facilitates the application of quantum simulation strategies to neural emulation.
A conceptual roadmap for implementing NeuroQ on near-term quantum platforms was outlined.

Conclusions:

NeuroQ offers a promising theoretical foundation for advancing brain emulation beyond classical limitations.
This quantum-inspired approach has potential implications for developing advanced neuromorphic quantum hardware.
The framework opens new avenues for research in artificial consciousness and cognitive architectures.