Learning to Correct Errors in Quantum Circuits via Transformer-Predicted PQCs

Practical quantum computing on Noisy Intermediate-Scale Quantum (NISQ) devices is fundamentally bottlenecked by hardware imperfections: errors accumulate quickly and can destroy the interference patterns that quantum algorithms rely on. While full quantum error correction promises fault tolerance, its overhead is prohibitive for near-term processors, and many quantum error mitigation techniques trade this limitation for substantial sampling cost, limited applicability, or per-circuit retraining. We propose an active, learning-based circuit-level correction framework that suppresses errors during execution. Our approach interleaves lightweight single-qubit parameterized quantum circuit (PQC) blocks into a target circuit and predicts their corrective rotation angles directly from the circuit's gate sequence. We formalize this setting as a general parameter prediction problem: learn a model that maps circuits to continuous corrective parameters to minimize the expected discrepancy between the ideal and corrected output distributions over the circuit domain. We implement this with a Transformer-Encoder architecture operating on a sliding-window circuit representation, enabling prediction of corrections in a single forward pass. Across diverse random benchmark circuits, the learned predictor achieves zero-shot correction on unseen instances, eliminating expensive per-circuit tuning at inference time. Empirically, our interleaved corrections substantially improve output distribution fidelity, maintaining state fidelities above 0.99 in regimes where uncorrected executions average between 0.3 and 0.5.