海森堡启发的概率计算的自旋矢量控制
Spin Vector Control for Heisenberg-Inspired Probabilistic Computing
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中文总结 AI 辅助
该研究提出一种自旋电子平台，通过双铁磁自旋注入单层石墨烯实现自旋矢量控制，为概率计算提供了可扩展的低功耗非布尔架构方案。
中文摘要 AI 辅助
概率比特（p-bits）已成为概率计算的基石，为概率推理和组合优化提供了高能效的硬件实现方案。将该领域推进至超越二元p-bits的关键挑战在于实现与操控矢量自旋信息，这是映射海森堡等基于能量的复杂模型的核心需求。本文展示了一种自旋电子平台，通过向单层石墨烯沟道注入双铁磁自旋，实现实空间矢量求和。通过独立控制注入电流电调谐自旋极化，我们实现了对合成自旋积累矢量的幅度和方向的连续控制。实验观察结合理论矢量求和模型与自旋电路模拟，揭示了自旋态的相干矢量相互作用和角度可调性。该方法可直接实现基于矢量的自旋逻辑，为使用随机低势垒磁体映射经典海森堡模型奠定了基础。我们的结果为实现基于二维材料的概率自旋电路建立了可扩展途径，为低功耗、非布尔计算架构提供了新机遇。
英文摘要
Probabilistic bits (p-bits) have emerged as a cornerstone of probabilistic computing, enabling energy-efficient hardware implementation for probabilistic inference and combinatorial optimization. A critical challenge in advancing this field beyond binary p-bits lies in realizing and manipulating vector spin information, essential for mapping complex energy-based models such as the Heisenberg Hamiltonian.Here, we demonstrate a spintronic platform capable of real-space vector summation by using dual ferromagnetic spin injections into a monolayer graphene channel. By electrically tuning the spin polarization through independently controlled injection currents, we achieve continuous control over the magnitude and direction of the resulting spin accumulation vector. Experimental observations, supported by theoretical vector summation models and spin-circuit simulations, reveal coherent vector interactions and angular tunability of the spin state. This approach enables direct implementation of vector-based spin logic and lays the groundwork for mapping classical Heisenberg models using stochastic low-barrier magnets. Our results establish a scalable pathway for realizing probabilistic spin circuits based on two-dimensional materials, offering new opportunities for low-power, non-Boolean computing architectures.
