AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL
AlphaClifford is introduced, a model-based Reinforcement Learning framework designed to efficiently synthesize Clifford circuits from the fundamental gate set composed of H, S, and CNOT, demonstrating the broad applicability of the framework on two additional tasks: hardware-constrained Clifford transpilation, where it outperform existing RL-based compilers, and as a post-synthesis optimization component within a full Clifford+T logical synthesis pipeline.
Daniele Lizzio Bosco, Jacopo Cossio, Carla Piazza et al.
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