An RL/GNN-based compiler (RLGS) finds emitter-based photonic graph-state generation sequences that reduce generation time by up to 57.5%, emitters by up to 17.5%, and CZ gates by up to 57.8% versus a Stabilizer Solver baseline.
Economou, and Shuo Sun
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Using Reinforcement Learning to Guide Graph State Generation for Photonic Quantum Computers
An RL/GNN-based compiler (RLGS) finds emitter-based photonic graph-state generation sequences that reduce generation time by up to 57.5%, emitters by up to 17.5%, and CZ gates by up to 57.8% versus a Stabilizer Solver baseline.