论文标题
部分可观测时空混沌系统的无模型预测
Spinsim: a GPU optimized python package for simulating spin-half and spin-one quantum systems
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
The Spinsim python package simulates spin-half and spin-one quantum mechanical systems following a time dependent Shroedinger equation. It makes use of numba.cuda, which is an LLVM (Low Level Virtual Machine) compiler for Nvidia Cuda compatible systems using GPU parallelization. Along with other optimizations, this allows for speed improvements from 3 to 4 orders of magnitude while staying just as accurate, compared to industry standard packages. It is available for installation on PyPI, and the source code is available on github. The initial use-case for the Spinsim will be to simulate quantum sensing-based ultracold atom experiments for the Monash University School of Physics \& Astronomy spinor Bose-Einstein condensate (spinor BEC) lab, but we anticipate it will be useful in simulating any range of spin-half or spin-one quantum systems with time dependent Hamiltonians that cannot be solved analytically. These appear in the fields of nuclear magnetic resonance (NMR), nuclear quadrupole resonance (NQR) and magnetic resonance imaging (MRI) experiments and quantum sensing, and with the spin-one systems of nitrogen vacancy centres (NVCs), ultracold atoms, and BECs.
