Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning

As NAND formula algorithm allow us to evaluate quality Quantum computing is a computation paradigm using of a position in a two-player game tree, we illustrate its properties of quantum mechanics to perform information potential application by using it as a training tool for a processing. Many famous quantum algorithms have been quantum agent in a simple two-player game. With the shown to outperform their equivalent classical algorithm[1], speed-up proposed by this algorithm, we are able to perform [2]. Quantum walk is a way to compose many promising evaluation of deeper trees in equivalent time (twice deeper quantum algorithms. It can be viewed as the quantum exploration for a binary tree). By using quantum algorithm analogues of classical random walks [3]. In several studies, to perform such explorations, we expect agents to achieve it has been shown that it could provide some algorithmic better performances in their learning process.