动态调节LMG量子比特群体的生物启发量子大脑
Dynamic Synaptic Modulation of LMG Qubits populations in a Bio-Inspired Quantum Brain
AI总结：
本文提出了一种生物启发的量子神经网络，利用LMG量子哈密顿量和突触反馈机制，实现神经元群体的稳态控制与节律生成。
摘要：
我们提出了一种生物启发的量子神经网络，将神经元群体编码为完全连接的量子比特，受Lipkin-Meshkov-Glick（LMG）量子哈密顿量支配，并通过突触效能反馈稳定，实现活动依赖的稳态控制。该框架将集体量子多体模式和吸引子结构与群体稳态和节律生成联系起来，概述了可扩展的计算原语——稳定的稳态点、可控的振荡和尺寸依赖的鲁棒性——使基于LMG的架构成为未来量子硬件上生物启发量子大脑的有前景的蓝图。
英文摘要：
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 modulated by a synaptic-efficacy feedback implementing activity-dependent changes in the collective time scale. The framework links collective quantum many-body modes and collective-state structure to population homeostasis and rhythmogenesis, outlining scalable computational primitives long-lived operating regimes, activity-dependent oscillation periods, 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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