利用张量列车形式实现蛋白质尺寸自旋系统的精确核磁共振模拟
Exact NMR simulation of protein-size spin systems using tensor train formalism
AI总结：
本文提出基于交替优化的张量列车方法，用于紧凑表示自旋哈密顿量和求解线性代数方程组，成功模拟了不含近似值的泛素15N核磁共振谱，优于传统方法的指数级复杂度。
摘要：
我们介绍了一种基于交替优化的新方法，用于在张量列车格式中实现自旋哈密顿量的紧凑表示和线性代数方程组的求解。我们通过模拟不含近似值的泛素15N核磁共振谱来展示该方法的实用性，该谱属于包含数百个相互作用核自旋的蛋白质。现有用于自旋系统和核磁共振实验的模拟算法要么需要显著近似，要么随自旋系统大小呈指数增长。我们比较了所提方法与使用启发式受限状态空间技术的Spinach包，后者实现多项式复杂度缩放。当自旋系统拓扑接近线性链（例如蛋白质的骨架）时，张量列车表示更紧凑，且使用受限状态空间的稀疏表示计算速度更慢。
英文摘要：
We introduce a new method, based on alternating optimization, for compact representation of spin Hamiltonians and solution of linear systems of algebraic equations in the tensor train format. We demonstrate the method's utility by simulating, without approximations, a 15N NMR spectrum of ubiquitin --- a protein containing several hundred interacting nuclear spins. Existing simulation algorithms for the spin system and the NMR experiment in question either require significant approximations or scale exponentially with the spin system size. We compare the proposed method to the Spinach package that uses heuristic restricted state space techniques to achieve polynomial complexity scaling. When the spin system topology is close to a linear chain (e.g. for the backbone of a protein), the tensor train representation is more compact and can be computed faster than the sparse representation using restricted state spaces.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
