Neural Error Mitigation of Near-Term Quantum Simulations
This addresses noise constraints in near-term quantum computing for applications in physics, chemistry, and materials science, representing an incremental improvement in error mitigation techniques.
The paper tackles the problem of noise and limited resources in near-term quantum simulations by introducing neural error mitigation, which improves estimates of ground states and observables, demonstrating low energy errors and high fidelities for molecular Hamiltonians and a lattice model without extra quantum resources.
Near-term quantum computers provide a promising platform for finding ground states of quantum systems, which is an essential task in physics, chemistry, and materials science. Near-term approaches, however, are constrained by the effects of noise as well as the limited resources of near-term quantum hardware. We introduce "neural error mitigation," which uses neural networks to improve estimates of ground states and ground-state observables obtained using near-term quantum simulations. To demonstrate our method's broad applicability, we employ neural error mitigation to find the ground states of the H$_2$ and LiH molecular Hamiltonians, as well as the lattice Schwinger model, prepared via the variational quantum eigensolver (VQE). Our results show that neural error mitigation improves numerical and experimental VQE computations to yield low energy errors, high fidelities, and accurate estimations of more-complex observables like order parameters and entanglement entropy, without requiring additional quantum resources. Furthermore, neural error mitigation is agnostic with respect to the quantum state preparation algorithm used, the quantum hardware it is implemented on, and the particular noise channel affecting the experiment, contributing to its versatility as a tool for quantum simulation.