AI RESEARCH
From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data
arXiv CS.LG
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ArXi:2605.01367v1 Announce Type: cross High-fidelity circuit execution on noisy intermediate-scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlated noise. To address this, this methodology article proposes a quantum machine learning control (QMLC) framework for generative quantum circuit synthesis from gate-set tomography (GST) data that bypasses the traditional two-step pipeline of characterizing native quantum gates via GST followed by unitary decomposition algorithms.