Vallverdú Jordi, Rius Gemma
Philosophy Department, ICREA-UAB, Bellaterra, 08193 Barcelona, Spain.
Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Bellaterra, 08193 Barcelona, Spain.
Biomimetics (Basel). 2025 Aug 7;10(8):516. doi: 10.3390/biomimetics10080516.
Traditional brain emulation approaches often rely on classical computational models that inadequately capture the stochastic, nonlinear, and potentially coherent features of biological neural systems. In this position paper, we introduce NeuroQ a quantum-inspired framework grounded in stochastic mechanics, particularly Nelson's formulation. By reformulating the FitzHugh-Nagumo neuron model with structured noise, we derive a Schrödinger-like equation that encodes membrane dynamics in a quantum-like formalism. This formulation enables the use of quantum simulation strategies-including Hamiltonian encoding, variational eigensolvers, and continuous-variable models-for neural emulation. We outline a conceptual roadmap for implementing NeuroQ on near-term quantum platforms and discuss its broader implications for neuromorphic quantum hardware, artificial consciousness, and time-symmetric cognitive architectures. Rather than demonstrating a working prototype, this work aims to establish a coherent theoretical foundation for future research in quantum brain emulation.
传统的大脑模拟方法通常依赖于经典计算模型，这些模型无法充分捕捉生物神经系统的随机、非线性和潜在的相干特征。在本立场文件中，我们引入了NeuroQ，这是一个基于随机力学，特别是尼尔森公式的量子启发框架。通过用结构化噪声重新表述菲茨休 - 纳古莫神经元模型，我们推导出了一个类似薛定谔方程，该方程以类似量子的形式编码膜动力学。这种表述使得能够使用量子模拟策略——包括哈密顿编码、变分特征求解器和连续变量模型——进行神经模拟。我们概述了在近期量子平台上实现NeuroQ的概念路线图，并讨论了其对神经形态量子硬件、人工意识和时间对称认知架构的更广泛影响。这项工作的目的不是展示一个可行的原型，而是为量子大脑模拟的未来研究建立一个连贯的理论基础。
