🤖 AI Summary
Quantum error mitigation is crucial for enhancing the performance of noisy intermediate-scale quantum (NISQ) devices, yet existing approaches rely either on prior noise models or access to ideal, noise-free training data. This work proposes a fully noise-agnostic neural-network-based error mitigation framework—requiring neither pre-specified noise parameters nor noiseless reference data. Its core innovation is quantum data augmentation: a self-supervised signal is constructed directly from noisy measurement outcomes to train the mitigator end-to-end. This enables robustness across diverse noise models and hardware platforms. The method applies broadly to quantum circuits, many-body systems, and continuous-variable setups. Extensive validation demonstrates significant improvements in measurement statistical fidelity—both in simulations of noisy quantum circuits and on real superconducting quantum processors. By eliminating the need for device-specific calibration or noise characterization, our approach establishes a practical, general-purpose, and calibration-free paradigm for quantum error mitigation on near-term quantum hardware.
📝 Abstract
Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural networks have the potential to lift this requirement, but current models require training data produced by ideal processes in the absence of noise. Here we build a neural model that achieves quantum error mitigation without any prior knowledge of the noise and without training on noise-free data. To achieve this feature, we introduce a quantum augmentation technique for error mitigation. Our approach applies to quantum circuits and to the dynamics of many-body and continuous-variable quantum systems, accommodating various types of noise models. We demonstrate its effectiveness by testing it both on simulated noisy circuits and on real quantum hardware.