如何教量子计算机概率分布
How to Teach a Quantum Computer a Probability Distribution
摘要：
目前，量子计算有三种主要范式，门模型，退火和图上行走。门模型和图上的量子行走是通用的计算模型，而退火在科学和数值计算的特定子集中发挥作用。然而，图上的量子行走并没有受到如此广泛的关注，因此新的应用和算法的出现大门是敞开的。在本文中，我们探索教学一个创造的离散时间量子行走的规则图的概率分布。我们通过两种方式来完成这个练习。首先，我们调整最大环面$\mathbb{T}^d$中的角度，其中$d$是图的正则性。其次，我们调整李代数$\mathfrak{su}(d)$的基的参数。我们还讨论了一些硬件和软件问题，以及直接应用程序和机器学习的几个连接。
英文摘要：
Currently there are three major paradigms of quantum computation, the gate model, annealing, and walks on graphs. The gate model and quantum walks on graphs are universal computation models, while annealing plays within a specific subset of scientific and numerical computations. Quantum walks on graphs have, however, not received such widespread attention and thus the door is wide open for new applications and algorithms to emerge. In this paper we explore teaching a coined discrete time quantum walk on a regular graph a probability distribution. We go through this exercise in two ways. First we adjust the angles in the maximal torus $\mathbb{T}^d$ where $d$ is the regularity of the graph. Second, we adjust the parameters of the basis of the Lie algebra $\mathfrak{su}(d)$. We also discuss some hardware and software concerns as well as immediate applications and the several connections to machine learning.
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