Entanglement is a key resource in many quantum information applications and achieving high values independently of the initial conditions is an important task. Here we address the problem of generating highly entangled states in a discrete time quantum walk irrespective of the initial state using two different approaches. First, we present and analyze a deterministic sequence of coin operators which produces high values of entanglement in a universal manner for a class of localized initial states. In a second approach, we optimize the discrete sequence of coin operators using a reinforcement learning algorithm. While the amount of entanglement produced by the deterministic sequence is fully independent of the initial states considered, the optimized sequences achieve in general higher average values of entanglement that do however depend on the initial state parameters. Our proposed sequence and optimization algorithm are especially useful in cases where the initial state is not...

中文翻译：

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一维离散时间量子游走中高纠缠生成的通用和最佳硬币序列

纠缠是许多量子信息应用中的关键资源，独立于初始条件而实现高价值是一项重要任务。在这里，我们解决了使用两种不同方法在离散时间量子行走中生成高纠缠态的问题，而与初始状态无关。首先，我们介绍并分析硬币算子的确定性序列，该序列以通用方式为一类局部初始状态产生高纠缠值。在第二种方法中，我们使用强化学习算法优化硬币运算符的离散序列。虽然确定性序列产生的纠缠量完全独立于所考虑的初始状态，最优化的序列通常获得更高的纠缠平均值，但这些平均值确实取决于初始状态参数。我们提出的序列和优化算法在初始状态不理想的情况下特别有用。