Enhanced extrapolation-based quantum error mitigation using repetitive structure in quantum algorithms

Abstract Quantum error mitigation is a key enabling technique for extracting reliable computational results from noisy intermediate-scale quantum devices. Among existing approaches, zero-noise extrapolation (ZNE) is widely employed, but its effectiveness deteriorates in high-noise regimes or deep circuits, where measurement outcomes converge to the random-guessing limit. To address this issue, we propose a lightweight, extrapolation-based error mitigation framework that leverages the intrinsic repetitive structure of quantum algorithms. We classify these algorithms into homogeneous and heterogeneous types and efficiently estimate block fidelities using shallow identity sequences of the core operational block, thereby avoiding global circuit folding. Experiments on IBM’s advanced superconducting platform and its corresponding simulator demonstrate that the proposed framework remains effective even in high-noise regimes where folding-based ZNE(f-ZNE) fails due to noise saturation. For a four-qubit Grover search, our approach restores the success probability to near its theoretical value, whereas f-ZNE yields negligible improvement. We further validate the proposed method on a 17-qubit quantum approximate optimization algorithm with layer-dependent parameters, demonstrating robustness beyond fixed-structure circuits. These results indicate that leveraging algorithmic structure provides a practical pathway to extending the performance of extrapolation-based error mitigation on current quantum processors.