论文标题
用于扰动分析的算法和量子动力学的模拟
Algorithms for perturbative analysis and simulation of quantum dynamics
论文作者
论文摘要
我们开发了用于计算和利用Dyson系列和Magnus扩展的通用算法,目的是促进量子动力学的数值扰动研究。为了对具有多个参数的模型进行广泛的应用程序,我们根据多变量灵敏度分析的算法,对于固定时间间隙的溶液或演变的时间平均发生器。这些工具同时计算到任意顺序的术语集合,并且从某种意义上说,模型可以以任意时间相关的方式取决于参数。我们在开源软件包\ qiskitdynamics {}中实现算法,利用JAX数组库来启用所有计算的及时汇编,自动差异化和GPU执行。使用单个Transmon的模型,我们演示了如何使用这些工具来近似模型参数空间区域的保真度,以及构造扰动鲁棒的控制目标。 我们还得出了最近引入的dysolve算法[Shillito等人,物理审查研究,3(3):033266]的基于Dyson和Magnus的变化,以模拟线性矩阵微分方程。我们展示了如何将策略步骤作为多变量扩展计算问题的词,而术语少于原始方法。当模拟GPU上的两透明纠缠门时,我们发现基于Dyson和Magnus的求解器提供了比传统ode求解器的加速度,从大约$ 2 \ times $ \ times $到$ 4 \ $ \ times for Solution $ \ $ 10 \ timple $ \ timple $ $ $ $ \ $ 60 \ $ \ times times times times times times times for solote solose cocifacy cecipaly cecipacy cecipacy cecipacy cecipacy revieface。
We develop general purpose algorithms for computing and utilizing both the Dyson series and Magnus expansion, with the goal of facilitating numerical perturbative studies of quantum dynamics. To enable broad applications to models with multiple parameters, we phrase our algorithms in terms of multivariable sensitivity analysis, for either the solution or the time-averaged generator of the evolution over a fixed time-interval. These tools simultaneously compute a collection of terms up to arbitrary order, and are general in the sense that the model can depend on the parameters in an arbitrary time-dependent way. We implement the algorithms in the open source software package \qiskitdynamics{}, utilizing the JAX array library to enable just-in-time compilation, automatic differentiation, and GPU execution of all computations. Using a model of a single transmon, we demonstrate how to use these tools to approximate fidelity in a region of model parameter space, as well as construct perturbative robust control objectives. We also derive and implement Dyson and Magnus-based variations of the recently introduced Dysolve algorithm [Shillito et al., Physical Review Research, 3(3):033266] for simulating linear matrix differential equations. We show how the pre-computation step can be phrased as a multivariable expansion computation problem with fewer terms than in the original method. When simulating a two-transmon entangling gate on a GPU, we find the Dyson and Magnus-based solvers provide a speedup over traditional ODE solvers, ranging from roughly $2\times$ to $4\times$ for a solution and $10\times$ to $60\times$ for a gradient, depending on solution accuracy.