Neural Error Mitigation of Near-Term Quantum Simulations

While variational methods for NISQ devices, such as VQE, are promising approaches to finding ground states of quantum systems relevant in physics, chemistry, and materials science, they are constrained by the effects of noise and device limitations, which motivates application of error mitigation techniques. We introduce neural error mitigation (NEM), a novel method that uses quantum many-body machine learning techniques to improve estimates of ground states and their observables obtained using VQE on noisy quantum devices. We apply NEM to finding molecular and lattice gauge theory ground states, and show that it improves numerical and experimental VQE results to yield low energy errors and infidelities, and accurate estimations of more complex observables, without requiring additional quantum resources. NEM is agnostic to the type of quantum hardware and the particular noise channel, and is therefore a promising versatile strategy for extending the reach of near-term quantum computers to solve complex quantum simulation problems.