  Quantum computers are actively competing to surpass classical supercomputers,
but quantum errors remain their chief obstacle. The key to overcoming these on
near-term devices has emerged through the field of quantum error mitigation,
enabling improved accuracy at the cost of additional runtime. In practice,
however, the success of mitigation is limited by a generally exponential
overhead. Can classical machine learning address this challenge on today's
quantum computers? Here, through both simulations and experiments on
state-of-the-art quantum computers using up to 100 qubits, we demonstrate that
machine learning for quantum error mitigation (ML-QEM) can drastically reduce
overheads, maintain or even surpass the accuracy of conventional methods, and
yield near noise-free results for quantum algorithms. We benchmark a variety of
machine learning models -- linear regression, random forests, multi-layer
perceptrons, and graph neural networks -- on diverse classes of quantum
circuits, over increasingly complex device-noise profiles, under interpolation
and extrapolation, and for small and large quantum circuits. These tests employ
the popular digital zero-noise extrapolation method as an added reference. We
further show how to scale ML-QEM to classically intractable quantum circuits by
mimicking the results of traditional mitigation results, while significantly
reducing overhead. Our results highlight the potential of classical machine
learning for practical quantum computation.
