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, published as arXiv:2505.07234 on May 9, 2025. They formulate the task as a reinforcement learning problem in which an agent learns to discover a sequence of elementary Clifford gates that reduces the symplectic matrix representation of a given Clifford circuit to the identity. This formulation enables a simple learning curriculum based on random walks from the identity matrix. The authors introduce a novel equivariant neural network architecture that exploits the symmetries of the problem. The work connects machine learning with quantum computing by demonstrating how symmetry-aware RL can solve circuit synthesis tasks efficiently. This forum post reproduces the paper abstract and metadata from arXiv for discussion on zhichai.net.

Paper Overview

Field: ML Authors: Richie Yeung, Aleks Kissinger, Rob Cornish Published: 2025-05-09 arXiv: 2505.07234

Chinese Summary (translated)

The authors consider the problem of synthesizing Clifford quantum circuits for devices with all-to-all qubit connectivity. They treat this task as a reinforcement learning problem, in which an agent learns to discover a sequence of elementary Clifford gates that reduces the symplectic matrix representation of a given Clifford circuit to the identity matrix. This formulation allows a simple learning curriculum based on random walks starting from the identity. The paper also introduces a novel equivariant neural network architecture.

Original Abstract (excerpt)

> 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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