🤖 AI Summary
High resource overhead impedes scalable fault-tolerant quantum computation.
Method: This paper proposes an efficient logical error suppression method leveraging sparse error characterization. Its core innovation is Cycle Error Reconstruction, which estimates only the top ~1% dominant Pauli error rates; combined with learnable low-dimensional physical error features, it enables accurate full error distribution reconstruction via heuristic distribution inference and maximum-likelihood decoding—bypassing the conventional requirement of a complete noise model.
Contribution/Results: Evaluated across multiple physically realistic noise models, the method achieves up to a 10× improvement in logical error performance over fidelity-based baseline decoders. It significantly reduces the resource overhead of quantum error correction, offering a practical pathway toward scalable fault-tolerant quantum computing.
📝 Abstract
Lowering the resource overhead needed to achieve fault-tolerant quantum computation is crucial to building scalable quantum computers. We show that adapting conventional maximum likelihood (ML) decoders to a small subset of efficiently learnable physical error characteristics can significantly improve the logical performance of a quantum error-correcting code. Specifically, we leverage error information obtained from efficient characterization methods based on Cycle Error Reconstruction (CER), which yields Pauli error rates on the $n$ qubits of an error-correcting code. Although the total number of Pauli error rates needed to describe a general noise process is exponentially large in $n$, we show that only a few of the largest few Pauli error rates are needed and that a heuristic technique can complete the Pauli error distribution for ML decoding from this restricted dataset. Using these techniques, we demonstrate significant performance improvements for decoding quantum codes under a variety of physically relevant error models. For instance, with CER data that constitute merely $1%$ of the Pauli error rates in the system, we achieve a $10X$ gain in performance compared to the case where decoding is based solely on the fidelity of the underlying noise process. Our conclusions underscore the promise of recent error characterization methods for improving quantum error correction and lowering overheads.