Transformer Hits 1.000 Fidelity on 6-Qubit Parameterized Circuits

Mehdi Saeedi, Eddie Richter, and Paul Hartke report achieving median fidelity of 1.000 on 3-6 qubit parameterized circuits using a hybrid approach that combines a 44.8 million-parameter transformer with classical optimization techniques. While this approach attains near-perfect fidelity on parameterized circuits, a limitation emerges when striving for exact equivalence in Clifford+T circuits. Though the model learns valid syntax and accurate T-gate statistics, accuracy degrades as circuit length increases, falling to near zero beyond 26 gates. This failure is termed autoregressive drift, early-token divergence cascading through left-to-right decoding, though inference-time strategies raising exact-match rates from 7% to 22.5% offer a partial solution. The researchers state the contrast is clear: the transformer succeeds when approximate outputs can be refined, but struggles with the need for exact discrete correctness. Achieving median fidelity of 1.000 on 3-6 qubit circuits represents a significant leap forward in quantum circuit optimization, as Mehdi Saeedi, Eddie Richter, and Paul Hartke…