[论文] Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthe...

论文概要

研究领域: ML 作者: Richie Yeung, Aleks Kissinger, Rob Cornish 发布时间: 2025-05-09 arXiv: 2505.07234

中文摘要

我们考虑为具有全对全量子比特连接的设备合成Clifford量子电路的问题。我们将这一任务作为强化学习问题来处理，其中一个智能体学习发现一系列基本Clifford门，将给定Clifford电路的辛矩阵表示约化为单位矩阵。这种表述允许基于从单位矩阵出发的随机游走的简单学习课程。我们引入了一种新颖的等变神经网络架构...

原文摘要

We consider the problem of synthesizing Clifford quantum circuits for devices with all-to-all qubit connectivity. We approach this task as a reinforcement learning problem in which an agent learns to discover a sequence of elementary Clifford gates that reduces a given symplectic matrix representation of a Clifford circuit to the identity. This formulation permits a simple learning curriculum based on random walks from the identity. We introduce a novel neural network architecture that is equiva...

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