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Framework for discrete-time quantum walks and a symmetric walk on a binary tree

机译:二叉树上的离散时间量子行走和对称行走的框架

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We formulate a framework for discrete-time quantum walks, motivated by classical random walks with memory. We present a specific representation of the classical walk with memory 2, on which this is based. The framework has no need for coin spaces, it imposes no constraints on the evolution operator other than unitarity,and is unifying of other approaches. As an example we construct a symmetric discrete-time quantum walk on the semi-infinite binary tree. The generating function of the amplitude at the root is computed in closed form, as a function of time and the initial level n in the tree, and we find the asymptotic and a full numerical solution for the amplitude. It exhibits a sharp interference peak and a power-law tail, as opposed to the exponentially decaying tail of a broadly peaked distribution of the classical symmetric random walk on a binary tree. The probability peak is orders of magnitude larger than it is for the classical walk (already at small n). The quantum walk shows a polynomial algorithmic speedup in n over the classical walk, which we conjecture to be of the order 2/3, based on strong trends in data.
机译:我们制定了一个离散时间量子游走的框架,该过程受具有记忆的经典随机游走的启发。我们提出了带有记忆2的经典步行的具体表示,这是基于此的。该框架不需要硬币空间,除了单一性外,它对演化算子没有任何限制,并且统一了其他方法。例如,我们在半无限二叉树上构造了一个对称的离散时间量子行走。根源处振幅的生成函数是封闭形式的,它是时间和树中初始水平n的函数,我们找到了该振幅的渐近和完整数值解。它表现出尖锐的干扰峰和幂律尾部,与二叉树上经典对称随机游走的宽峰分布的指数衰减尾部相反。概率峰值比经典步行(已经小n)大几个数量级。量子步态在经典步态上显示了n的多项式算法加速,基于数据的强劲趋势,我们推测其为2/3阶。

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