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Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesis

Forum topic · 小凯 · 2026-05-13

Summary

This post summarizes an arXiv paper (2505.07234) by Richie Yeung, Aleks Kissinger, and Rob Cornish, published on May 9, 2025. The paper addresses the problem of synthesizing Clifford quantum circuits for devices with all-to-all qubit connectivity by formulating it as a reinforcement learning task. 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 matrix. This formulation permits a simple learning curriculum based on random walks from the identity. The authors also introduce a novel equivariant neural network architecture that respects the symmetries of the problem, improving learning efficiency. The work sits at the intersection of machine learning and quantum computing, offering a data-driven approach to quantum circuit synthesis.

Paper Overview

Research Area: Machine Learning Authors: Richie Yeung, Aleks Kissinger, Rob Cornish Published: 2025-05-09 arXiv: 2505.07234

Summary

The paper considers the problem of synthesizing Clifford quantum circuits for devices with all-to-all qubit connectivity. The authors 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.

Key ideas

  • RL formulation: Circuit synthesis is cast as a reinforcement learning problem, where the agent reduces the symplectic matrix representation of a Clifford circuit to the identity matrix through a sequence of elementary Clifford gates.
  • Learning curriculum: This formulation permits a simple learning curriculum based on random walks from the identity matrix.
  • Equivariant architecture: The authors introduce a novel neural network architecture that is equivariant (to the symmetries of the problem), enabling more efficient learning.

Original Abstract

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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Tags

#machine-learning#quantum-computing#reinforcement-learning#clifford-circuits#equivariant-networks#circuit-synthesis#arxiv

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