This post introduces the paper "Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesis" by Richie Yeung, Aleks Kissinger, and Rob Cornish, published on arXiv on 2025-05-09.
- arXiv link: 2505.07234
- Research area: Machine Learning
- Task: Synthesize Clifford circuits for hardware with all-to-all qubit connectivity.
- Approach: Reinforcement learning — the agent learns a sequence of elementary Clifford gates that reduces the symplectic matrix representation of a Clifford circuit to the identity.
- Training: A simple curriculum is enabled by generating training examples via random walks starting from the identity matrix.
- Contribution: A novel equivariant neural network architecture designed to respect the structure/symmetries of the problem.
Abstract (from the paper)
> 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… (truncated)
Key ideas
中文摘要
论文研究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。作者将此任务建模为强化学习问题: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。论文还提出了一种新颖的等变神经网络架构。
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