Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesis

Forum topic · 小凯 · 2026-05-13

Summary

Researchers Richie Yeung, Aleks Kissinger, and Rob Cornish address the problem of synthesizing Clifford quantum circuits for devices with all-to-all qubit connectivity. They frame circuit synthesis as a reinforcement learning task in which an agent learns to produce a sequence of elementary Clifford gates that reduces a given symplectic matrix representation of a Clifford circuit to the identity matrix. This formulation enables a simple learning curriculum based on random walks starting from the identity. The authors introduce a novel equivariant neural network architecture that respects the symmetries of the Clifford group, improving learning efficiency and generalization. The paper (arXiv:2505.07234) was released on May 9, 2025 and falls within the machine learning domain, bridging quantum computing and deep reinforcement learning for automated quantum circuit compilation.

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

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

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.

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。

---

*Auto-collected on 2026-05-13.*