AI 中文总结
提出基于Oja流的量子模型降阶新方法，开发两种算法，为绝热消除提供非微扰替代方案，可用于量子信息处理的噪声保护子空间码，在中心自旋模型上验证了方法有效性。
AI 中文摘要
我们提出一种新方法，无需微扰迭代即可数值推导马尔可夫量子开放系统的近似降阶动力学模型，将演化投影到与最慢自由度相关的子空间。我们开发的两种算法基于Oja连续时间主成分流：第一种返回最慢衰减算子子空间的最优降阶，可扩展到时变动力学；第二种旨在降低系统希尔伯特空间子空间上的动力学，从而保持条件完全正定性。这些方法是成熟绝热消除（AE）方法的非微扰替代方案，第二种方法可用于寻找量子信息处理的噪声保护子空间码。两种方法均在典型的中心自旋模型上进行了测试。
英文摘要
We propose a novel approach to numerically derive approximate reduced dynamical models for Markovian quantum open systems without perturbative iterations, projecting the evolution to the subspace associated to their slowest degrees of freedom. The two algorithms we develop are based on Oja's continuous-time principal component flow: the first returns the optimal reduction to the slowest decaying operator-subspace, and is extended to time-dependent dynamics, while the second one is designed to reduce the dynamics on a subspace of the system's Hilbert space, and thus preserve conditional complete positivity. The methods represent a non-perturbative alternative to well-established Adiabatic Elimination (AE) methods, and the second can be used to find noise-protected subspace codes for quantum information processing. Both are tested on a paradigmatic central spin model.
