优化ruby晶格上的量子自旋湖泊的动态制备
Optimizing the dynamical preparation of quantum spin lakes on the ruby lattice
AI总结：
该研究通过神经量子态模拟，优化了ruby晶格中量子自旋湖泊的动态制备，揭示了其拓扑性质和长度尺度特征。
摘要：
量子自旋液体是难以捉摸的长程纠缠态。受Rydberg量子模拟器实验的启发，近期的研究重点集中在即使基态相图中没有这样的拓扑相，也能动态制备具有量子自旋液体相关性的状态的可能性上。理解此类量子自旋“湖”态的微观性质及其与平衡自旋液体秩序的关系仍然是一个关键问题。在此，我们扩展了近似对称的神经量子态在实时演化中的应用，并直接模拟了最多N=384个原子系统的动态制备。我们分析了多种自旋液体诊断指标，作为制备协议函数，并优化了所获得的量子自旋湖的范围。在最优情况下，制备的状态显示出自旋液体性质延伸至系统大小的一半，拓扑纠缠熵接近γ=ln 2的平台。我们提取出两个物理长度尺度λ和ξ，它们从上和下限制了量子自旋湖的范围ℓ。
英文摘要：
Quantum spin liquids are elusive long-range entangled states. Motivated by experiments in Rydberg quantum simulators, recent excitement has centered on the possibility of dynamically preparing a state with quantum spin liquid correlation even when the ground state phase diagram does not exhibit such a topological phase. Understanding the microscopic nature of such quantum spin "lake" states and their relationship to equilibrium spin liquid order remains an essential question. Here, we extend the use of approximately symmetric neural quantum states for real-time evolution and directly simulate the dynamical preparation in systems of up to $N=384$ atoms. We analyze a variety of spin liquid diagnostics as a function of the preparation protocol and optimize the extent of the quantum spin lake thus obtained. In the optimal case, the prepared state shows spin-liquid properties extending over half the system size, with a topological entanglement entropy plateauing close to $γ= \ln 2$. We extract two physical length scales $λ$ and $ξ$ which constrain the extent of the quantum spin lake $\ell$ from above and below.
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。
