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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