Quantum computers have progressed towards outperforming classical supercomputers, but quantum errors remain the primary obstacle. In the past few years, the field of quantum error mitigation has provided strategies for overcoming errors in near-term devices, enabling improved accuracy at the cost of additional run time. Through experiments on state-of-the-art quantum computers using up to 100 qubits, we demonstrate that without sacrificing accuracy, machine learning for quantum error mitigation (ML-QEM) drastically reduces the cost of mitigation. We benchmarked ML-QEM using a variety of machine learning models—linear regression, random forest, multilayer perceptron and graph neural networks—on diverse classes of quantum circuits, over increasingly complex device noise profiles, under interpolation and extrapolation, and in both numerics and experiments. These tests employed the popular digital zero-noise extrapolation method as an added reference. Finally, we propose a path towards scalable mitigation using ML-QEM to mimic traditional mitigation methods with superior runtime efficiency. Our results show that classical machine learning can extend the reach and practicality of quantum error mitigation by reducing its overhead and highlight its broader potential for practical quantum computations.

量子计算机的性能已经超越了经典超级计算机，但量子错误仍然是主要障碍。在过去几年中，量子误差缓解领域为克服近期器件中的误差提供了策略，以额外的运行时间为代价提高了精度。通过使用多达 100 个量子比特的最先进的量子计算机上进行实验，我们证明，在不牺牲准确性的情况下，用于量子错误缓解 （ML-QEM） 的机器学习大大降低了缓解成本。我们使用各种机器学习模型（线性回归、随机森林、多层感知器和图形神经网络）在不同类别的量子电路、日益复杂的器件噪声曲线、插值和外推以及数值和实验中对 ML-QEM 进行了基准测试。这些测试采用了流行的数字零噪声外推方法作为附加参考。最后，我们提出了一种使用 ML-QEM 实现可扩展缓解的途径，以模拟具有卓越运行效率的传统缓解方法。我们的结果表明，经典机器学习可以通过减少其开销来扩展量子错误缓解的范围和实用性，并突出其在实际量子计算中的更广泛潜力。