论文标题
有效的电路实施，用于在二元树上造成的量子步行和加固学习的应用
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning
论文作者
论文摘要
在许多量子算法中使用量子散步在二进制树上，以实现与经典算法相比的重要加速。作为量子电路时，这种算法的表述具有易于读取的优势，可在基于电路的量子计算机和模拟器上执行，并最佳地使用资源。我们提出了一种策略，以构成量子电路，该量子电路遵循通用门模型量子计算原理在二进制树上进行量子行走。我们特别关注NAND公式评估算法，因为它可能在游戏理论和强化学习中具有许多应用。因此，我们提出了该算法的应用，并展示如何在两个玩家游戏环境中使用量子加固学习代理。
Quantum walks on binary trees are used in many quantum algorithms to achieve important speedup over classical algorithms. The formulation of this kind of algorithms as quantum circuit presents the advantage of being easily readable, executable on circuit based quantum computers and simulators and optimal on the usage of resources. We propose a strategy to compose quantum circuit that performs quantum walk on binary trees following universal gate model quantum computation principles. We give a particular attention to NAND formula evaluation algorithm as it could have many applications in game theory and reinforcement learning. We therefore propose an application of this algorithm and show how it can be used to train a quantum reinforcement learning agent in a two player game environment.
